Embed Size (px)
Citation preview
A Systems Scientistrsquos Thoughts on Model-Based Systems Engineering
Wayne Wakeland PhDSystems Science ProgramPortland State University
The Nature of Systems
bull Dynamicbull Connectedcoupledbull Governed by feedbackbull Boundaries are artificial (often permeable)bull Complex amp non-linearbull History dependentbull Edge of chaos
bull Self-organizingbull Self-replicating (living systems)bull Adaptiveevolutionarybull Characterized by trade-offsbull Counterintuitivebull Policy resistantbull Emergent
In complex systems cause and effect are often distant in time and space
We may act to produce short-term benefits and long-term costs we forget about delay
The solution to one problem may cause another problem (unintended results)
Artist Gary Larson
Example
The rdquoGreen Revolutionrdquo agricultural technologies were introduced into Asia in the late 1960s as a solution for food insecurity Decades later they have proved detrimental in terms of biodiversity loss increased use of agro-chemical based pest and weed control water logging salinization and land degradation
Slide adapted from LEAD International and Sustainability Institute
Events
Patternsof Behavior
SystemicStructure
Mental Models
The Iceberg EVENTS ndash PATTERNS ndash STRUCTURE
REACT
ANTICIPATE
DESIGN
Increased leverage and opportunities
for learning
What happened
What has been happening
Why has this been happeningHow can I improve the performance of the system
Systems Science Methods
bull Systems science methods focus on understanding the general properties and behavior of complex systems by creating models and finding patterns in data
bull We strive to interact effectively with collaborators from various disciplines by using clear and well-annotated graphics and diagrams to present models and data analysis results
Key Concepts and Principlesbull A system consists of elements amp relationships with
specific purposegoalfunction bull Whole gt sum of the partsbull Structure causes behaviorbull Circular causality
ndash Outputs influence inputs cannot separate cause and effectbull Mental models (often hidden) shape our thinkingbull Systems Archetypes (common structures amp behaviors)
ndash Eg Fixes that fail Shifting the Burden Success to the Successful Limits to Growth Tragedy of the Commons
bull And much more (far too much to cover this morning)ndash Complex adaptive systems living systems open systems
structural coupling autopoiesis adaptation resilience evolution hellip
Systems Thinkingbull Seeing the forest and the treesbull Interconnectednessbull Thinking dynamically
ndash Behavior over timendash Delayed impactsconsequences
bull Thinking closed loop (vs linear causality)bull Endogenous thinking (system as cause)bull Thinking operationally
ndash How things actually actually work
Useful Communications Tools Causal Loop Diagrams
Example Fixes that Fail Archetype
bull The story due to budget problems spending on maintenance decreases which balances the budgethellipBUT over time breakdowns increase forcing more spending which stresses the budget even worse than before
Iteration in Systems DesignIntervention Processes
Specific Modeling Methods bull System dynamics
ndash Focuses on modeling the underlying feedback structures with differential equations
ndash Equation are solved to simulate behavior over timebull Discrete system simulation
ndash Uses a Monte Carlo approach to analyze how the varietyrandomness impacts system performance
ndash Often emphasizing business operations and processes especially in manufacturing and supply chain logistics
bull Agent based simulationndash Used to study how low-level interactions between
individual agents influences overall system behaviorperformance
System Dynamics Example
bull Project Managementndash Brooksrsquo Law ldquoAdding manpower to a late software
project makes it laterrdquobull SD has been used to simulate complex
projects and evaluate potential decisions actions policies
Typical Project ldquoDisastersrdquobull 1048708 The Channel Tunnel -- original estimatebull $3 billion final cost $10 billionbull 1048708 Bostonrsquos ldquoBig Digrdquo -- original mid-bull 1980rsquos estimate $25 billion latestbull estimate $145 billion (92001)bull 1048708 Aircraft development -- nearly doublebull initial estimate bull 1048708 New Car Development -- original planbull 400 person-years of effort final cost 800bull person-years
Project Behavior Over Time
Discrete System Simulationbull Detailed step-by-step emulation of the flow of entities
through the systemndash With uncertain arrivals processing times andor routing
(branching)bull The computer monitors each simulated entity as simulated
time proceedsndash Enter system move thru according to the various probability
fns governing timing and sequence of eventsbull The computer also records pertinent data regarding the
simulated entities and serversndash wait times throughput queue lengths process times
utilizationhellipbull Creates a synthetic sample of system performance databull Sample data is then analyzed statistically
Types of Problems DSS can Addressbull Performance issues in existing systems
ndash Long waits high inventory poor utilization of resources low throughput
bull Need to estimate performance of a system under designbull What Might One Learn
ndash Where the bottlenecks are and how they might be alleviatedndash How to improve flow reduce queues and wait times and
increase utilization amp throughputndash The optimal number of servers queues buffers etcndash Effective operating rules or policies
Examples of Discrete System Simulationbull Manufacturing facilitiesbull Bank operationsbull Airport operations
(passengers security planes crews baggage)
bull Transportationlogistics distribution operations
bull Hospital facilities (emergency room operating room admissions)
bull Computer networkbull Freeway systembull Business process (eg
insurance office)bull Criminal justice
systembull Chemical plantbull Fast-food restaurantbull Supermarketbull Theme parkbull Emergency Response
system
Project for Systems Science 527 Class Spring 2011
Manufacturing Process Design Drawing
DSS model animation(closely mimics the actual system)
The model contains complex logic regarding A) Different fault occurrences B) Part filling requirements and C) Realistic variations seen in complex assembly processes
Model Resultsbull The simulation showed the behavior of the
proposed new automated systembull It suggested that given expected faults operator
utilization will be 45-55bull Thus if the operator must load parts do audits
and perform fault correction they could not handle two machines as would be needed to achieve the cost targets
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
The Nature of Systems
bull Dynamicbull Connectedcoupledbull Governed by feedbackbull Boundaries are artificial (often permeable)bull Complex amp non-linearbull History dependentbull Edge of chaos
bull Self-organizingbull Self-replicating (living systems)bull Adaptiveevolutionarybull Characterized by trade-offsbull Counterintuitivebull Policy resistantbull Emergent
In complex systems cause and effect are often distant in time and space
We may act to produce short-term benefits and long-term costs we forget about delay
The solution to one problem may cause another problem (unintended results)
Artist Gary Larson
Example
The rdquoGreen Revolutionrdquo agricultural technologies were introduced into Asia in the late 1960s as a solution for food insecurity Decades later they have proved detrimental in terms of biodiversity loss increased use of agro-chemical based pest and weed control water logging salinization and land degradation
Slide adapted from LEAD International and Sustainability Institute
Events
Patternsof Behavior
SystemicStructure
Mental Models
The Iceberg EVENTS ndash PATTERNS ndash STRUCTURE
REACT
ANTICIPATE
DESIGN
Increased leverage and opportunities
for learning
What happened
What has been happening
Why has this been happeningHow can I improve the performance of the system
Systems Science Methods
bull Systems science methods focus on understanding the general properties and behavior of complex systems by creating models and finding patterns in data
bull We strive to interact effectively with collaborators from various disciplines by using clear and well-annotated graphics and diagrams to present models and data analysis results
Key Concepts and Principlesbull A system consists of elements amp relationships with
specific purposegoalfunction bull Whole gt sum of the partsbull Structure causes behaviorbull Circular causality
ndash Outputs influence inputs cannot separate cause and effectbull Mental models (often hidden) shape our thinkingbull Systems Archetypes (common structures amp behaviors)
ndash Eg Fixes that fail Shifting the Burden Success to the Successful Limits to Growth Tragedy of the Commons
bull And much more (far too much to cover this morning)ndash Complex adaptive systems living systems open systems
structural coupling autopoiesis adaptation resilience evolution hellip
Systems Thinkingbull Seeing the forest and the treesbull Interconnectednessbull Thinking dynamically
ndash Behavior over timendash Delayed impactsconsequences
bull Thinking closed loop (vs linear causality)bull Endogenous thinking (system as cause)bull Thinking operationally
ndash How things actually actually work
Useful Communications Tools Causal Loop Diagrams
Example Fixes that Fail Archetype
bull The story due to budget problems spending on maintenance decreases which balances the budgethellipBUT over time breakdowns increase forcing more spending which stresses the budget even worse than before
Iteration in Systems DesignIntervention Processes
Specific Modeling Methods bull System dynamics
ndash Focuses on modeling the underlying feedback structures with differential equations
ndash Equation are solved to simulate behavior over timebull Discrete system simulation
ndash Uses a Monte Carlo approach to analyze how the varietyrandomness impacts system performance
ndash Often emphasizing business operations and processes especially in manufacturing and supply chain logistics
bull Agent based simulationndash Used to study how low-level interactions between
individual agents influences overall system behaviorperformance
System Dynamics Example
bull Project Managementndash Brooksrsquo Law ldquoAdding manpower to a late software
project makes it laterrdquobull SD has been used to simulate complex
projects and evaluate potential decisions actions policies
Typical Project ldquoDisastersrdquobull 1048708 The Channel Tunnel -- original estimatebull $3 billion final cost $10 billionbull 1048708 Bostonrsquos ldquoBig Digrdquo -- original mid-bull 1980rsquos estimate $25 billion latestbull estimate $145 billion (92001)bull 1048708 Aircraft development -- nearly doublebull initial estimate bull 1048708 New Car Development -- original planbull 400 person-years of effort final cost 800bull person-years
Project Behavior Over Time
Discrete System Simulationbull Detailed step-by-step emulation of the flow of entities
through the systemndash With uncertain arrivals processing times andor routing
(branching)bull The computer monitors each simulated entity as simulated
time proceedsndash Enter system move thru according to the various probability
fns governing timing and sequence of eventsbull The computer also records pertinent data regarding the
simulated entities and serversndash wait times throughput queue lengths process times
utilizationhellipbull Creates a synthetic sample of system performance databull Sample data is then analyzed statistically
Types of Problems DSS can Addressbull Performance issues in existing systems
ndash Long waits high inventory poor utilization of resources low throughput
bull Need to estimate performance of a system under designbull What Might One Learn
ndash Where the bottlenecks are and how they might be alleviatedndash How to improve flow reduce queues and wait times and
increase utilization amp throughputndash The optimal number of servers queues buffers etcndash Effective operating rules or policies
Examples of Discrete System Simulationbull Manufacturing facilitiesbull Bank operationsbull Airport operations
(passengers security planes crews baggage)
bull Transportationlogistics distribution operations
bull Hospital facilities (emergency room operating room admissions)
bull Computer networkbull Freeway systembull Business process (eg
insurance office)bull Criminal justice
systembull Chemical plantbull Fast-food restaurantbull Supermarketbull Theme parkbull Emergency Response
system
Project for Systems Science 527 Class Spring 2011
Manufacturing Process Design Drawing
DSS model animation(closely mimics the actual system)
The model contains complex logic regarding A) Different fault occurrences B) Part filling requirements and C) Realistic variations seen in complex assembly processes
Model Resultsbull The simulation showed the behavior of the
proposed new automated systembull It suggested that given expected faults operator
utilization will be 45-55bull Thus if the operator must load parts do audits
and perform fault correction they could not handle two machines as would be needed to achieve the cost targets
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
In complex systems cause and effect are often distant in time and space
We may act to produce short-term benefits and long-term costs we forget about delay
The solution to one problem may cause another problem (unintended results)
Artist Gary Larson
Example
The rdquoGreen Revolutionrdquo agricultural technologies were introduced into Asia in the late 1960s as a solution for food insecurity Decades later they have proved detrimental in terms of biodiversity loss increased use of agro-chemical based pest and weed control water logging salinization and land degradation
Slide adapted from LEAD International and Sustainability Institute
Events
Patternsof Behavior
SystemicStructure
Mental Models
The Iceberg EVENTS ndash PATTERNS ndash STRUCTURE
REACT
ANTICIPATE
DESIGN
Increased leverage and opportunities
for learning
What happened
What has been happening
Why has this been happeningHow can I improve the performance of the system
Systems Science Methods
bull Systems science methods focus on understanding the general properties and behavior of complex systems by creating models and finding patterns in data
bull We strive to interact effectively with collaborators from various disciplines by using clear and well-annotated graphics and diagrams to present models and data analysis results
Key Concepts and Principlesbull A system consists of elements amp relationships with
specific purposegoalfunction bull Whole gt sum of the partsbull Structure causes behaviorbull Circular causality
ndash Outputs influence inputs cannot separate cause and effectbull Mental models (often hidden) shape our thinkingbull Systems Archetypes (common structures amp behaviors)
ndash Eg Fixes that fail Shifting the Burden Success to the Successful Limits to Growth Tragedy of the Commons
bull And much more (far too much to cover this morning)ndash Complex adaptive systems living systems open systems
structural coupling autopoiesis adaptation resilience evolution hellip
Systems Thinkingbull Seeing the forest and the treesbull Interconnectednessbull Thinking dynamically
ndash Behavior over timendash Delayed impactsconsequences
bull Thinking closed loop (vs linear causality)bull Endogenous thinking (system as cause)bull Thinking operationally
ndash How things actually actually work
Useful Communications Tools Causal Loop Diagrams
Example Fixes that Fail Archetype
bull The story due to budget problems spending on maintenance decreases which balances the budgethellipBUT over time breakdowns increase forcing more spending which stresses the budget even worse than before
Iteration in Systems DesignIntervention Processes
Specific Modeling Methods bull System dynamics
ndash Focuses on modeling the underlying feedback structures with differential equations
ndash Equation are solved to simulate behavior over timebull Discrete system simulation
ndash Uses a Monte Carlo approach to analyze how the varietyrandomness impacts system performance
ndash Often emphasizing business operations and processes especially in manufacturing and supply chain logistics
bull Agent based simulationndash Used to study how low-level interactions between
individual agents influences overall system behaviorperformance
System Dynamics Example
bull Project Managementndash Brooksrsquo Law ldquoAdding manpower to a late software
project makes it laterrdquobull SD has been used to simulate complex
projects and evaluate potential decisions actions policies
Typical Project ldquoDisastersrdquobull 1048708 The Channel Tunnel -- original estimatebull $3 billion final cost $10 billionbull 1048708 Bostonrsquos ldquoBig Digrdquo -- original mid-bull 1980rsquos estimate $25 billion latestbull estimate $145 billion (92001)bull 1048708 Aircraft development -- nearly doublebull initial estimate bull 1048708 New Car Development -- original planbull 400 person-years of effort final cost 800bull person-years
Project Behavior Over Time
Discrete System Simulationbull Detailed step-by-step emulation of the flow of entities
through the systemndash With uncertain arrivals processing times andor routing
(branching)bull The computer monitors each simulated entity as simulated
time proceedsndash Enter system move thru according to the various probability
fns governing timing and sequence of eventsbull The computer also records pertinent data regarding the
simulated entities and serversndash wait times throughput queue lengths process times
utilizationhellipbull Creates a synthetic sample of system performance databull Sample data is then analyzed statistically
Types of Problems DSS can Addressbull Performance issues in existing systems
ndash Long waits high inventory poor utilization of resources low throughput
bull Need to estimate performance of a system under designbull What Might One Learn
ndash Where the bottlenecks are and how they might be alleviatedndash How to improve flow reduce queues and wait times and
increase utilization amp throughputndash The optimal number of servers queues buffers etcndash Effective operating rules or policies
Examples of Discrete System Simulationbull Manufacturing facilitiesbull Bank operationsbull Airport operations
(passengers security planes crews baggage)
bull Transportationlogistics distribution operations
bull Hospital facilities (emergency room operating room admissions)
bull Computer networkbull Freeway systembull Business process (eg
insurance office)bull Criminal justice
systembull Chemical plantbull Fast-food restaurantbull Supermarketbull Theme parkbull Emergency Response
system
Project for Systems Science 527 Class Spring 2011
Manufacturing Process Design Drawing
DSS model animation(closely mimics the actual system)
The model contains complex logic regarding A) Different fault occurrences B) Part filling requirements and C) Realistic variations seen in complex assembly processes
Model Resultsbull The simulation showed the behavior of the
proposed new automated systembull It suggested that given expected faults operator
utilization will be 45-55bull Thus if the operator must load parts do audits
and perform fault correction they could not handle two machines as would be needed to achieve the cost targets
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
The solution to one problem may cause another problem (unintended results)
Artist Gary Larson
Example
The rdquoGreen Revolutionrdquo agricultural technologies were introduced into Asia in the late 1960s as a solution for food insecurity Decades later they have proved detrimental in terms of biodiversity loss increased use of agro-chemical based pest and weed control water logging salinization and land degradation
Slide adapted from LEAD International and Sustainability Institute
Events
Patternsof Behavior
SystemicStructure
Mental Models
The Iceberg EVENTS ndash PATTERNS ndash STRUCTURE
REACT
ANTICIPATE
DESIGN
Increased leverage and opportunities
for learning
What happened
What has been happening
Why has this been happeningHow can I improve the performance of the system
Systems Science Methods
bull Systems science methods focus on understanding the general properties and behavior of complex systems by creating models and finding patterns in data
bull We strive to interact effectively with collaborators from various disciplines by using clear and well-annotated graphics and diagrams to present models and data analysis results
Key Concepts and Principlesbull A system consists of elements amp relationships with
specific purposegoalfunction bull Whole gt sum of the partsbull Structure causes behaviorbull Circular causality
ndash Outputs influence inputs cannot separate cause and effectbull Mental models (often hidden) shape our thinkingbull Systems Archetypes (common structures amp behaviors)
ndash Eg Fixes that fail Shifting the Burden Success to the Successful Limits to Growth Tragedy of the Commons
bull And much more (far too much to cover this morning)ndash Complex adaptive systems living systems open systems
structural coupling autopoiesis adaptation resilience evolution hellip
Systems Thinkingbull Seeing the forest and the treesbull Interconnectednessbull Thinking dynamically
ndash Behavior over timendash Delayed impactsconsequences
bull Thinking closed loop (vs linear causality)bull Endogenous thinking (system as cause)bull Thinking operationally
ndash How things actually actually work
Useful Communications Tools Causal Loop Diagrams
Example Fixes that Fail Archetype
bull The story due to budget problems spending on maintenance decreases which balances the budgethellipBUT over time breakdowns increase forcing more spending which stresses the budget even worse than before
Iteration in Systems DesignIntervention Processes
Specific Modeling Methods bull System dynamics
ndash Focuses on modeling the underlying feedback structures with differential equations
ndash Equation are solved to simulate behavior over timebull Discrete system simulation
ndash Uses a Monte Carlo approach to analyze how the varietyrandomness impacts system performance
ndash Often emphasizing business operations and processes especially in manufacturing and supply chain logistics
bull Agent based simulationndash Used to study how low-level interactions between
individual agents influences overall system behaviorperformance
System Dynamics Example
bull Project Managementndash Brooksrsquo Law ldquoAdding manpower to a late software
project makes it laterrdquobull SD has been used to simulate complex
projects and evaluate potential decisions actions policies
Typical Project ldquoDisastersrdquobull 1048708 The Channel Tunnel -- original estimatebull $3 billion final cost $10 billionbull 1048708 Bostonrsquos ldquoBig Digrdquo -- original mid-bull 1980rsquos estimate $25 billion latestbull estimate $145 billion (92001)bull 1048708 Aircraft development -- nearly doublebull initial estimate bull 1048708 New Car Development -- original planbull 400 person-years of effort final cost 800bull person-years
Project Behavior Over Time
Discrete System Simulationbull Detailed step-by-step emulation of the flow of entities
through the systemndash With uncertain arrivals processing times andor routing
(branching)bull The computer monitors each simulated entity as simulated
time proceedsndash Enter system move thru according to the various probability
fns governing timing and sequence of eventsbull The computer also records pertinent data regarding the
simulated entities and serversndash wait times throughput queue lengths process times
utilizationhellipbull Creates a synthetic sample of system performance databull Sample data is then analyzed statistically
Types of Problems DSS can Addressbull Performance issues in existing systems
ndash Long waits high inventory poor utilization of resources low throughput
bull Need to estimate performance of a system under designbull What Might One Learn
ndash Where the bottlenecks are and how they might be alleviatedndash How to improve flow reduce queues and wait times and
increase utilization amp throughputndash The optimal number of servers queues buffers etcndash Effective operating rules or policies
Examples of Discrete System Simulationbull Manufacturing facilitiesbull Bank operationsbull Airport operations
(passengers security planes crews baggage)
bull Transportationlogistics distribution operations
bull Hospital facilities (emergency room operating room admissions)
bull Computer networkbull Freeway systembull Business process (eg
insurance office)bull Criminal justice
systembull Chemical plantbull Fast-food restaurantbull Supermarketbull Theme parkbull Emergency Response
system
Project for Systems Science 527 Class Spring 2011
Manufacturing Process Design Drawing
DSS model animation(closely mimics the actual system)
The model contains complex logic regarding A) Different fault occurrences B) Part filling requirements and C) Realistic variations seen in complex assembly processes
Model Resultsbull The simulation showed the behavior of the
proposed new automated systembull It suggested that given expected faults operator
utilization will be 45-55bull Thus if the operator must load parts do audits
and perform fault correction they could not handle two machines as would be needed to achieve the cost targets
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Events
Patternsof Behavior
SystemicStructure
Mental Models
The Iceberg EVENTS ndash PATTERNS ndash STRUCTURE
REACT
ANTICIPATE
DESIGN
Increased leverage and opportunities
for learning
What happened
What has been happening
Why has this been happeningHow can I improve the performance of the system
Systems Science Methods
bull Systems science methods focus on understanding the general properties and behavior of complex systems by creating models and finding patterns in data
bull We strive to interact effectively with collaborators from various disciplines by using clear and well-annotated graphics and diagrams to present models and data analysis results
Key Concepts and Principlesbull A system consists of elements amp relationships with
specific purposegoalfunction bull Whole gt sum of the partsbull Structure causes behaviorbull Circular causality
ndash Outputs influence inputs cannot separate cause and effectbull Mental models (often hidden) shape our thinkingbull Systems Archetypes (common structures amp behaviors)
ndash Eg Fixes that fail Shifting the Burden Success to the Successful Limits to Growth Tragedy of the Commons
bull And much more (far too much to cover this morning)ndash Complex adaptive systems living systems open systems
structural coupling autopoiesis adaptation resilience evolution hellip
Systems Thinkingbull Seeing the forest and the treesbull Interconnectednessbull Thinking dynamically
ndash Behavior over timendash Delayed impactsconsequences
bull Thinking closed loop (vs linear causality)bull Endogenous thinking (system as cause)bull Thinking operationally
ndash How things actually actually work
Useful Communications Tools Causal Loop Diagrams
Example Fixes that Fail Archetype
bull The story due to budget problems spending on maintenance decreases which balances the budgethellipBUT over time breakdowns increase forcing more spending which stresses the budget even worse than before
Iteration in Systems DesignIntervention Processes
Specific Modeling Methods bull System dynamics
ndash Focuses on modeling the underlying feedback structures with differential equations
ndash Equation are solved to simulate behavior over timebull Discrete system simulation
ndash Uses a Monte Carlo approach to analyze how the varietyrandomness impacts system performance
ndash Often emphasizing business operations and processes especially in manufacturing and supply chain logistics
bull Agent based simulationndash Used to study how low-level interactions between
individual agents influences overall system behaviorperformance
System Dynamics Example
bull Project Managementndash Brooksrsquo Law ldquoAdding manpower to a late software
project makes it laterrdquobull SD has been used to simulate complex
projects and evaluate potential decisions actions policies
Typical Project ldquoDisastersrdquobull 1048708 The Channel Tunnel -- original estimatebull $3 billion final cost $10 billionbull 1048708 Bostonrsquos ldquoBig Digrdquo -- original mid-bull 1980rsquos estimate $25 billion latestbull estimate $145 billion (92001)bull 1048708 Aircraft development -- nearly doublebull initial estimate bull 1048708 New Car Development -- original planbull 400 person-years of effort final cost 800bull person-years
Project Behavior Over Time
Discrete System Simulationbull Detailed step-by-step emulation of the flow of entities
through the systemndash With uncertain arrivals processing times andor routing
(branching)bull The computer monitors each simulated entity as simulated
time proceedsndash Enter system move thru according to the various probability
fns governing timing and sequence of eventsbull The computer also records pertinent data regarding the
simulated entities and serversndash wait times throughput queue lengths process times
utilizationhellipbull Creates a synthetic sample of system performance databull Sample data is then analyzed statistically
Types of Problems DSS can Addressbull Performance issues in existing systems
ndash Long waits high inventory poor utilization of resources low throughput
bull Need to estimate performance of a system under designbull What Might One Learn
ndash Where the bottlenecks are and how they might be alleviatedndash How to improve flow reduce queues and wait times and
increase utilization amp throughputndash The optimal number of servers queues buffers etcndash Effective operating rules or policies
Examples of Discrete System Simulationbull Manufacturing facilitiesbull Bank operationsbull Airport operations
(passengers security planes crews baggage)
bull Transportationlogistics distribution operations
bull Hospital facilities (emergency room operating room admissions)
bull Computer networkbull Freeway systembull Business process (eg
insurance office)bull Criminal justice
systembull Chemical plantbull Fast-food restaurantbull Supermarketbull Theme parkbull Emergency Response
system
Project for Systems Science 527 Class Spring 2011
Manufacturing Process Design Drawing
DSS model animation(closely mimics the actual system)
The model contains complex logic regarding A) Different fault occurrences B) Part filling requirements and C) Realistic variations seen in complex assembly processes
Model Resultsbull The simulation showed the behavior of the
proposed new automated systembull It suggested that given expected faults operator
utilization will be 45-55bull Thus if the operator must load parts do audits
and perform fault correction they could not handle two machines as would be needed to achieve the cost targets
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Systems Science Methods
bull Systems science methods focus on understanding the general properties and behavior of complex systems by creating models and finding patterns in data
bull We strive to interact effectively with collaborators from various disciplines by using clear and well-annotated graphics and diagrams to present models and data analysis results
Key Concepts and Principlesbull A system consists of elements amp relationships with
specific purposegoalfunction bull Whole gt sum of the partsbull Structure causes behaviorbull Circular causality
ndash Outputs influence inputs cannot separate cause and effectbull Mental models (often hidden) shape our thinkingbull Systems Archetypes (common structures amp behaviors)
ndash Eg Fixes that fail Shifting the Burden Success to the Successful Limits to Growth Tragedy of the Commons
bull And much more (far too much to cover this morning)ndash Complex adaptive systems living systems open systems
structural coupling autopoiesis adaptation resilience evolution hellip
Systems Thinkingbull Seeing the forest and the treesbull Interconnectednessbull Thinking dynamically
ndash Behavior over timendash Delayed impactsconsequences
bull Thinking closed loop (vs linear causality)bull Endogenous thinking (system as cause)bull Thinking operationally
ndash How things actually actually work
Useful Communications Tools Causal Loop Diagrams
Example Fixes that Fail Archetype
bull The story due to budget problems spending on maintenance decreases which balances the budgethellipBUT over time breakdowns increase forcing more spending which stresses the budget even worse than before
Iteration in Systems DesignIntervention Processes
Specific Modeling Methods bull System dynamics
ndash Focuses on modeling the underlying feedback structures with differential equations
ndash Equation are solved to simulate behavior over timebull Discrete system simulation
ndash Uses a Monte Carlo approach to analyze how the varietyrandomness impacts system performance
ndash Often emphasizing business operations and processes especially in manufacturing and supply chain logistics
bull Agent based simulationndash Used to study how low-level interactions between
individual agents influences overall system behaviorperformance
System Dynamics Example
bull Project Managementndash Brooksrsquo Law ldquoAdding manpower to a late software
project makes it laterrdquobull SD has been used to simulate complex
projects and evaluate potential decisions actions policies
Typical Project ldquoDisastersrdquobull 1048708 The Channel Tunnel -- original estimatebull $3 billion final cost $10 billionbull 1048708 Bostonrsquos ldquoBig Digrdquo -- original mid-bull 1980rsquos estimate $25 billion latestbull estimate $145 billion (92001)bull 1048708 Aircraft development -- nearly doublebull initial estimate bull 1048708 New Car Development -- original planbull 400 person-years of effort final cost 800bull person-years
Project Behavior Over Time
Discrete System Simulationbull Detailed step-by-step emulation of the flow of entities
through the systemndash With uncertain arrivals processing times andor routing
(branching)bull The computer monitors each simulated entity as simulated
time proceedsndash Enter system move thru according to the various probability
fns governing timing and sequence of eventsbull The computer also records pertinent data regarding the
simulated entities and serversndash wait times throughput queue lengths process times
utilizationhellipbull Creates a synthetic sample of system performance databull Sample data is then analyzed statistically
Types of Problems DSS can Addressbull Performance issues in existing systems
ndash Long waits high inventory poor utilization of resources low throughput
bull Need to estimate performance of a system under designbull What Might One Learn
ndash Where the bottlenecks are and how they might be alleviatedndash How to improve flow reduce queues and wait times and
increase utilization amp throughputndash The optimal number of servers queues buffers etcndash Effective operating rules or policies
Examples of Discrete System Simulationbull Manufacturing facilitiesbull Bank operationsbull Airport operations
(passengers security planes crews baggage)
bull Transportationlogistics distribution operations
bull Hospital facilities (emergency room operating room admissions)
bull Computer networkbull Freeway systembull Business process (eg
insurance office)bull Criminal justice
systembull Chemical plantbull Fast-food restaurantbull Supermarketbull Theme parkbull Emergency Response
system
Project for Systems Science 527 Class Spring 2011
Manufacturing Process Design Drawing
DSS model animation(closely mimics the actual system)
The model contains complex logic regarding A) Different fault occurrences B) Part filling requirements and C) Realistic variations seen in complex assembly processes
Model Resultsbull The simulation showed the behavior of the
proposed new automated systembull It suggested that given expected faults operator
utilization will be 45-55bull Thus if the operator must load parts do audits
and perform fault correction they could not handle two machines as would be needed to achieve the cost targets
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Key Concepts and Principlesbull A system consists of elements amp relationships with
specific purposegoalfunction bull Whole gt sum of the partsbull Structure causes behaviorbull Circular causality
ndash Outputs influence inputs cannot separate cause and effectbull Mental models (often hidden) shape our thinkingbull Systems Archetypes (common structures amp behaviors)
ndash Eg Fixes that fail Shifting the Burden Success to the Successful Limits to Growth Tragedy of the Commons
bull And much more (far too much to cover this morning)ndash Complex adaptive systems living systems open systems
structural coupling autopoiesis adaptation resilience evolution hellip
Systems Thinkingbull Seeing the forest and the treesbull Interconnectednessbull Thinking dynamically
ndash Behavior over timendash Delayed impactsconsequences
bull Thinking closed loop (vs linear causality)bull Endogenous thinking (system as cause)bull Thinking operationally
ndash How things actually actually work
Useful Communications Tools Causal Loop Diagrams
Example Fixes that Fail Archetype
bull The story due to budget problems spending on maintenance decreases which balances the budgethellipBUT over time breakdowns increase forcing more spending which stresses the budget even worse than before
Iteration in Systems DesignIntervention Processes
Specific Modeling Methods bull System dynamics
ndash Focuses on modeling the underlying feedback structures with differential equations
ndash Equation are solved to simulate behavior over timebull Discrete system simulation
ndash Uses a Monte Carlo approach to analyze how the varietyrandomness impacts system performance
ndash Often emphasizing business operations and processes especially in manufacturing and supply chain logistics
bull Agent based simulationndash Used to study how low-level interactions between
individual agents influences overall system behaviorperformance
System Dynamics Example
bull Project Managementndash Brooksrsquo Law ldquoAdding manpower to a late software
project makes it laterrdquobull SD has been used to simulate complex
projects and evaluate potential decisions actions policies
Typical Project ldquoDisastersrdquobull 1048708 The Channel Tunnel -- original estimatebull $3 billion final cost $10 billionbull 1048708 Bostonrsquos ldquoBig Digrdquo -- original mid-bull 1980rsquos estimate $25 billion latestbull estimate $145 billion (92001)bull 1048708 Aircraft development -- nearly doublebull initial estimate bull 1048708 New Car Development -- original planbull 400 person-years of effort final cost 800bull person-years
Project Behavior Over Time
Discrete System Simulationbull Detailed step-by-step emulation of the flow of entities
through the systemndash With uncertain arrivals processing times andor routing
(branching)bull The computer monitors each simulated entity as simulated
time proceedsndash Enter system move thru according to the various probability
fns governing timing and sequence of eventsbull The computer also records pertinent data regarding the
simulated entities and serversndash wait times throughput queue lengths process times
utilizationhellipbull Creates a synthetic sample of system performance databull Sample data is then analyzed statistically
Types of Problems DSS can Addressbull Performance issues in existing systems
ndash Long waits high inventory poor utilization of resources low throughput
bull Need to estimate performance of a system under designbull What Might One Learn
ndash Where the bottlenecks are and how they might be alleviatedndash How to improve flow reduce queues and wait times and
increase utilization amp throughputndash The optimal number of servers queues buffers etcndash Effective operating rules or policies
Examples of Discrete System Simulationbull Manufacturing facilitiesbull Bank operationsbull Airport operations
(passengers security planes crews baggage)
bull Transportationlogistics distribution operations
bull Hospital facilities (emergency room operating room admissions)
bull Computer networkbull Freeway systembull Business process (eg
insurance office)bull Criminal justice
systembull Chemical plantbull Fast-food restaurantbull Supermarketbull Theme parkbull Emergency Response
system
Project for Systems Science 527 Class Spring 2011
Manufacturing Process Design Drawing
DSS model animation(closely mimics the actual system)
The model contains complex logic regarding A) Different fault occurrences B) Part filling requirements and C) Realistic variations seen in complex assembly processes
Model Resultsbull The simulation showed the behavior of the
proposed new automated systembull It suggested that given expected faults operator
utilization will be 45-55bull Thus if the operator must load parts do audits
and perform fault correction they could not handle two machines as would be needed to achieve the cost targets
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Systems Thinkingbull Seeing the forest and the treesbull Interconnectednessbull Thinking dynamically
ndash Behavior over timendash Delayed impactsconsequences
bull Thinking closed loop (vs linear causality)bull Endogenous thinking (system as cause)bull Thinking operationally
ndash How things actually actually work
Useful Communications Tools Causal Loop Diagrams
Example Fixes that Fail Archetype
bull The story due to budget problems spending on maintenance decreases which balances the budgethellipBUT over time breakdowns increase forcing more spending which stresses the budget even worse than before
Iteration in Systems DesignIntervention Processes
Specific Modeling Methods bull System dynamics
ndash Focuses on modeling the underlying feedback structures with differential equations
ndash Equation are solved to simulate behavior over timebull Discrete system simulation
ndash Uses a Monte Carlo approach to analyze how the varietyrandomness impacts system performance
ndash Often emphasizing business operations and processes especially in manufacturing and supply chain logistics
bull Agent based simulationndash Used to study how low-level interactions between
individual agents influences overall system behaviorperformance
System Dynamics Example
bull Project Managementndash Brooksrsquo Law ldquoAdding manpower to a late software
project makes it laterrdquobull SD has been used to simulate complex
projects and evaluate potential decisions actions policies
Typical Project ldquoDisastersrdquobull 1048708 The Channel Tunnel -- original estimatebull $3 billion final cost $10 billionbull 1048708 Bostonrsquos ldquoBig Digrdquo -- original mid-bull 1980rsquos estimate $25 billion latestbull estimate $145 billion (92001)bull 1048708 Aircraft development -- nearly doublebull initial estimate bull 1048708 New Car Development -- original planbull 400 person-years of effort final cost 800bull person-years
Project Behavior Over Time
Discrete System Simulationbull Detailed step-by-step emulation of the flow of entities
through the systemndash With uncertain arrivals processing times andor routing
(branching)bull The computer monitors each simulated entity as simulated
time proceedsndash Enter system move thru according to the various probability
fns governing timing and sequence of eventsbull The computer also records pertinent data regarding the
simulated entities and serversndash wait times throughput queue lengths process times
utilizationhellipbull Creates a synthetic sample of system performance databull Sample data is then analyzed statistically
Types of Problems DSS can Addressbull Performance issues in existing systems
ndash Long waits high inventory poor utilization of resources low throughput
bull Need to estimate performance of a system under designbull What Might One Learn
ndash Where the bottlenecks are and how they might be alleviatedndash How to improve flow reduce queues and wait times and
increase utilization amp throughputndash The optimal number of servers queues buffers etcndash Effective operating rules or policies
Examples of Discrete System Simulationbull Manufacturing facilitiesbull Bank operationsbull Airport operations
(passengers security planes crews baggage)
bull Transportationlogistics distribution operations
bull Hospital facilities (emergency room operating room admissions)
bull Computer networkbull Freeway systembull Business process (eg
insurance office)bull Criminal justice
systembull Chemical plantbull Fast-food restaurantbull Supermarketbull Theme parkbull Emergency Response
system
Project for Systems Science 527 Class Spring 2011
Manufacturing Process Design Drawing
DSS model animation(closely mimics the actual system)
The model contains complex logic regarding A) Different fault occurrences B) Part filling requirements and C) Realistic variations seen in complex assembly processes
Model Resultsbull The simulation showed the behavior of the
proposed new automated systembull It suggested that given expected faults operator
utilization will be 45-55bull Thus if the operator must load parts do audits
and perform fault correction they could not handle two machines as would be needed to achieve the cost targets
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Useful Communications Tools Causal Loop Diagrams
Example Fixes that Fail Archetype
bull The story due to budget problems spending on maintenance decreases which balances the budgethellipBUT over time breakdowns increase forcing more spending which stresses the budget even worse than before
Iteration in Systems DesignIntervention Processes
Specific Modeling Methods bull System dynamics
ndash Focuses on modeling the underlying feedback structures with differential equations
ndash Equation are solved to simulate behavior over timebull Discrete system simulation
ndash Uses a Monte Carlo approach to analyze how the varietyrandomness impacts system performance
ndash Often emphasizing business operations and processes especially in manufacturing and supply chain logistics
bull Agent based simulationndash Used to study how low-level interactions between
individual agents influences overall system behaviorperformance
System Dynamics Example
bull Project Managementndash Brooksrsquo Law ldquoAdding manpower to a late software
project makes it laterrdquobull SD has been used to simulate complex
projects and evaluate potential decisions actions policies
Typical Project ldquoDisastersrdquobull 1048708 The Channel Tunnel -- original estimatebull $3 billion final cost $10 billionbull 1048708 Bostonrsquos ldquoBig Digrdquo -- original mid-bull 1980rsquos estimate $25 billion latestbull estimate $145 billion (92001)bull 1048708 Aircraft development -- nearly doublebull initial estimate bull 1048708 New Car Development -- original planbull 400 person-years of effort final cost 800bull person-years
Project Behavior Over Time
Discrete System Simulationbull Detailed step-by-step emulation of the flow of entities
through the systemndash With uncertain arrivals processing times andor routing
(branching)bull The computer monitors each simulated entity as simulated
time proceedsndash Enter system move thru according to the various probability
fns governing timing and sequence of eventsbull The computer also records pertinent data regarding the
simulated entities and serversndash wait times throughput queue lengths process times
utilizationhellipbull Creates a synthetic sample of system performance databull Sample data is then analyzed statistically
Types of Problems DSS can Addressbull Performance issues in existing systems
ndash Long waits high inventory poor utilization of resources low throughput
bull Need to estimate performance of a system under designbull What Might One Learn
ndash Where the bottlenecks are and how they might be alleviatedndash How to improve flow reduce queues and wait times and
increase utilization amp throughputndash The optimal number of servers queues buffers etcndash Effective operating rules or policies
Examples of Discrete System Simulationbull Manufacturing facilitiesbull Bank operationsbull Airport operations
(passengers security planes crews baggage)
bull Transportationlogistics distribution operations
bull Hospital facilities (emergency room operating room admissions)
bull Computer networkbull Freeway systembull Business process (eg
insurance office)bull Criminal justice
systembull Chemical plantbull Fast-food restaurantbull Supermarketbull Theme parkbull Emergency Response
system
Project for Systems Science 527 Class Spring 2011
Manufacturing Process Design Drawing
DSS model animation(closely mimics the actual system)
The model contains complex logic regarding A) Different fault occurrences B) Part filling requirements and C) Realistic variations seen in complex assembly processes
Model Resultsbull The simulation showed the behavior of the
proposed new automated systembull It suggested that given expected faults operator
utilization will be 45-55bull Thus if the operator must load parts do audits
and perform fault correction they could not handle two machines as would be needed to achieve the cost targets
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Example Fixes that Fail Archetype
bull The story due to budget problems spending on maintenance decreases which balances the budgethellipBUT over time breakdowns increase forcing more spending which stresses the budget even worse than before
Iteration in Systems DesignIntervention Processes
Specific Modeling Methods bull System dynamics
ndash Focuses on modeling the underlying feedback structures with differential equations
ndash Equation are solved to simulate behavior over timebull Discrete system simulation
ndash Uses a Monte Carlo approach to analyze how the varietyrandomness impacts system performance
ndash Often emphasizing business operations and processes especially in manufacturing and supply chain logistics
bull Agent based simulationndash Used to study how low-level interactions between
individual agents influences overall system behaviorperformance
System Dynamics Example
bull Project Managementndash Brooksrsquo Law ldquoAdding manpower to a late software
project makes it laterrdquobull SD has been used to simulate complex
projects and evaluate potential decisions actions policies
Typical Project ldquoDisastersrdquobull 1048708 The Channel Tunnel -- original estimatebull $3 billion final cost $10 billionbull 1048708 Bostonrsquos ldquoBig Digrdquo -- original mid-bull 1980rsquos estimate $25 billion latestbull estimate $145 billion (92001)bull 1048708 Aircraft development -- nearly doublebull initial estimate bull 1048708 New Car Development -- original planbull 400 person-years of effort final cost 800bull person-years
Project Behavior Over Time
Discrete System Simulationbull Detailed step-by-step emulation of the flow of entities
through the systemndash With uncertain arrivals processing times andor routing
(branching)bull The computer monitors each simulated entity as simulated
time proceedsndash Enter system move thru according to the various probability
fns governing timing and sequence of eventsbull The computer also records pertinent data regarding the
simulated entities and serversndash wait times throughput queue lengths process times
utilizationhellipbull Creates a synthetic sample of system performance databull Sample data is then analyzed statistically
Types of Problems DSS can Addressbull Performance issues in existing systems
ndash Long waits high inventory poor utilization of resources low throughput
bull Need to estimate performance of a system under designbull What Might One Learn
ndash Where the bottlenecks are and how they might be alleviatedndash How to improve flow reduce queues and wait times and
increase utilization amp throughputndash The optimal number of servers queues buffers etcndash Effective operating rules or policies
Examples of Discrete System Simulationbull Manufacturing facilitiesbull Bank operationsbull Airport operations
(passengers security planes crews baggage)
bull Transportationlogistics distribution operations
bull Hospital facilities (emergency room operating room admissions)
bull Computer networkbull Freeway systembull Business process (eg
insurance office)bull Criminal justice
systembull Chemical plantbull Fast-food restaurantbull Supermarketbull Theme parkbull Emergency Response
system
Project for Systems Science 527 Class Spring 2011
Manufacturing Process Design Drawing
DSS model animation(closely mimics the actual system)
The model contains complex logic regarding A) Different fault occurrences B) Part filling requirements and C) Realistic variations seen in complex assembly processes
Model Resultsbull The simulation showed the behavior of the
proposed new automated systembull It suggested that given expected faults operator
utilization will be 45-55bull Thus if the operator must load parts do audits
and perform fault correction they could not handle two machines as would be needed to achieve the cost targets
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Iteration in Systems DesignIntervention Processes
Specific Modeling Methods bull System dynamics
ndash Focuses on modeling the underlying feedback structures with differential equations
ndash Equation are solved to simulate behavior over timebull Discrete system simulation
ndash Uses a Monte Carlo approach to analyze how the varietyrandomness impacts system performance
ndash Often emphasizing business operations and processes especially in manufacturing and supply chain logistics
bull Agent based simulationndash Used to study how low-level interactions between
individual agents influences overall system behaviorperformance
System Dynamics Example
bull Project Managementndash Brooksrsquo Law ldquoAdding manpower to a late software
project makes it laterrdquobull SD has been used to simulate complex
projects and evaluate potential decisions actions policies
Typical Project ldquoDisastersrdquobull 1048708 The Channel Tunnel -- original estimatebull $3 billion final cost $10 billionbull 1048708 Bostonrsquos ldquoBig Digrdquo -- original mid-bull 1980rsquos estimate $25 billion latestbull estimate $145 billion (92001)bull 1048708 Aircraft development -- nearly doublebull initial estimate bull 1048708 New Car Development -- original planbull 400 person-years of effort final cost 800bull person-years
Project Behavior Over Time
Discrete System Simulationbull Detailed step-by-step emulation of the flow of entities
through the systemndash With uncertain arrivals processing times andor routing
(branching)bull The computer monitors each simulated entity as simulated
time proceedsndash Enter system move thru according to the various probability
fns governing timing and sequence of eventsbull The computer also records pertinent data regarding the
simulated entities and serversndash wait times throughput queue lengths process times
utilizationhellipbull Creates a synthetic sample of system performance databull Sample data is then analyzed statistically
Types of Problems DSS can Addressbull Performance issues in existing systems
ndash Long waits high inventory poor utilization of resources low throughput
bull Need to estimate performance of a system under designbull What Might One Learn
ndash Where the bottlenecks are and how they might be alleviatedndash How to improve flow reduce queues and wait times and
increase utilization amp throughputndash The optimal number of servers queues buffers etcndash Effective operating rules or policies
Examples of Discrete System Simulationbull Manufacturing facilitiesbull Bank operationsbull Airport operations
(passengers security planes crews baggage)
bull Transportationlogistics distribution operations
bull Hospital facilities (emergency room operating room admissions)
bull Computer networkbull Freeway systembull Business process (eg
insurance office)bull Criminal justice
systembull Chemical plantbull Fast-food restaurantbull Supermarketbull Theme parkbull Emergency Response
system
Project for Systems Science 527 Class Spring 2011
Manufacturing Process Design Drawing
DSS model animation(closely mimics the actual system)
The model contains complex logic regarding A) Different fault occurrences B) Part filling requirements and C) Realistic variations seen in complex assembly processes
Model Resultsbull The simulation showed the behavior of the
proposed new automated systembull It suggested that given expected faults operator
utilization will be 45-55bull Thus if the operator must load parts do audits
and perform fault correction they could not handle two machines as would be needed to achieve the cost targets
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Specific Modeling Methods bull System dynamics
ndash Focuses on modeling the underlying feedback structures with differential equations
ndash Equation are solved to simulate behavior over timebull Discrete system simulation
ndash Uses a Monte Carlo approach to analyze how the varietyrandomness impacts system performance
ndash Often emphasizing business operations and processes especially in manufacturing and supply chain logistics
bull Agent based simulationndash Used to study how low-level interactions between
individual agents influences overall system behaviorperformance
System Dynamics Example
bull Project Managementndash Brooksrsquo Law ldquoAdding manpower to a late software
project makes it laterrdquobull SD has been used to simulate complex
projects and evaluate potential decisions actions policies
Typical Project ldquoDisastersrdquobull 1048708 The Channel Tunnel -- original estimatebull $3 billion final cost $10 billionbull 1048708 Bostonrsquos ldquoBig Digrdquo -- original mid-bull 1980rsquos estimate $25 billion latestbull estimate $145 billion (92001)bull 1048708 Aircraft development -- nearly doublebull initial estimate bull 1048708 New Car Development -- original planbull 400 person-years of effort final cost 800bull person-years
Project Behavior Over Time
Discrete System Simulationbull Detailed step-by-step emulation of the flow of entities
through the systemndash With uncertain arrivals processing times andor routing
(branching)bull The computer monitors each simulated entity as simulated
time proceedsndash Enter system move thru according to the various probability
fns governing timing and sequence of eventsbull The computer also records pertinent data regarding the
simulated entities and serversndash wait times throughput queue lengths process times
utilizationhellipbull Creates a synthetic sample of system performance databull Sample data is then analyzed statistically
Types of Problems DSS can Addressbull Performance issues in existing systems
ndash Long waits high inventory poor utilization of resources low throughput
bull Need to estimate performance of a system under designbull What Might One Learn
ndash Where the bottlenecks are and how they might be alleviatedndash How to improve flow reduce queues and wait times and
increase utilization amp throughputndash The optimal number of servers queues buffers etcndash Effective operating rules or policies
Examples of Discrete System Simulationbull Manufacturing facilitiesbull Bank operationsbull Airport operations
(passengers security planes crews baggage)
bull Transportationlogistics distribution operations
bull Hospital facilities (emergency room operating room admissions)
bull Computer networkbull Freeway systembull Business process (eg
insurance office)bull Criminal justice
systembull Chemical plantbull Fast-food restaurantbull Supermarketbull Theme parkbull Emergency Response
system
Project for Systems Science 527 Class Spring 2011
Manufacturing Process Design Drawing
DSS model animation(closely mimics the actual system)
The model contains complex logic regarding A) Different fault occurrences B) Part filling requirements and C) Realistic variations seen in complex assembly processes
Model Resultsbull The simulation showed the behavior of the
proposed new automated systembull It suggested that given expected faults operator
utilization will be 45-55bull Thus if the operator must load parts do audits
and perform fault correction they could not handle two machines as would be needed to achieve the cost targets
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
System Dynamics Example
bull Project Managementndash Brooksrsquo Law ldquoAdding manpower to a late software
project makes it laterrdquobull SD has been used to simulate complex
projects and evaluate potential decisions actions policies
Typical Project ldquoDisastersrdquobull 1048708 The Channel Tunnel -- original estimatebull $3 billion final cost $10 billionbull 1048708 Bostonrsquos ldquoBig Digrdquo -- original mid-bull 1980rsquos estimate $25 billion latestbull estimate $145 billion (92001)bull 1048708 Aircraft development -- nearly doublebull initial estimate bull 1048708 New Car Development -- original planbull 400 person-years of effort final cost 800bull person-years
Project Behavior Over Time
Discrete System Simulationbull Detailed step-by-step emulation of the flow of entities
through the systemndash With uncertain arrivals processing times andor routing
(branching)bull The computer monitors each simulated entity as simulated
time proceedsndash Enter system move thru according to the various probability
fns governing timing and sequence of eventsbull The computer also records pertinent data regarding the
simulated entities and serversndash wait times throughput queue lengths process times
utilizationhellipbull Creates a synthetic sample of system performance databull Sample data is then analyzed statistically
Types of Problems DSS can Addressbull Performance issues in existing systems
ndash Long waits high inventory poor utilization of resources low throughput
bull Need to estimate performance of a system under designbull What Might One Learn
ndash Where the bottlenecks are and how they might be alleviatedndash How to improve flow reduce queues and wait times and
increase utilization amp throughputndash The optimal number of servers queues buffers etcndash Effective operating rules or policies
Examples of Discrete System Simulationbull Manufacturing facilitiesbull Bank operationsbull Airport operations
(passengers security planes crews baggage)
bull Transportationlogistics distribution operations
bull Hospital facilities (emergency room operating room admissions)
bull Computer networkbull Freeway systembull Business process (eg
insurance office)bull Criminal justice
systembull Chemical plantbull Fast-food restaurantbull Supermarketbull Theme parkbull Emergency Response
system
Project for Systems Science 527 Class Spring 2011
Manufacturing Process Design Drawing
DSS model animation(closely mimics the actual system)
The model contains complex logic regarding A) Different fault occurrences B) Part filling requirements and C) Realistic variations seen in complex assembly processes
Model Resultsbull The simulation showed the behavior of the
proposed new automated systembull It suggested that given expected faults operator
utilization will be 45-55bull Thus if the operator must load parts do audits
and perform fault correction they could not handle two machines as would be needed to achieve the cost targets
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Typical Project ldquoDisastersrdquobull 1048708 The Channel Tunnel -- original estimatebull $3 billion final cost $10 billionbull 1048708 Bostonrsquos ldquoBig Digrdquo -- original mid-bull 1980rsquos estimate $25 billion latestbull estimate $145 billion (92001)bull 1048708 Aircraft development -- nearly doublebull initial estimate bull 1048708 New Car Development -- original planbull 400 person-years of effort final cost 800bull person-years
Project Behavior Over Time
Discrete System Simulationbull Detailed step-by-step emulation of the flow of entities
through the systemndash With uncertain arrivals processing times andor routing
(branching)bull The computer monitors each simulated entity as simulated
time proceedsndash Enter system move thru according to the various probability
fns governing timing and sequence of eventsbull The computer also records pertinent data regarding the
simulated entities and serversndash wait times throughput queue lengths process times
utilizationhellipbull Creates a synthetic sample of system performance databull Sample data is then analyzed statistically
Types of Problems DSS can Addressbull Performance issues in existing systems
ndash Long waits high inventory poor utilization of resources low throughput
bull Need to estimate performance of a system under designbull What Might One Learn
ndash Where the bottlenecks are and how they might be alleviatedndash How to improve flow reduce queues and wait times and
increase utilization amp throughputndash The optimal number of servers queues buffers etcndash Effective operating rules or policies
Examples of Discrete System Simulationbull Manufacturing facilitiesbull Bank operationsbull Airport operations
(passengers security planes crews baggage)
bull Transportationlogistics distribution operations
bull Hospital facilities (emergency room operating room admissions)
bull Computer networkbull Freeway systembull Business process (eg
insurance office)bull Criminal justice
systembull Chemical plantbull Fast-food restaurantbull Supermarketbull Theme parkbull Emergency Response
system
Project for Systems Science 527 Class Spring 2011
Manufacturing Process Design Drawing
DSS model animation(closely mimics the actual system)
The model contains complex logic regarding A) Different fault occurrences B) Part filling requirements and C) Realistic variations seen in complex assembly processes
Model Resultsbull The simulation showed the behavior of the
proposed new automated systembull It suggested that given expected faults operator
utilization will be 45-55bull Thus if the operator must load parts do audits
and perform fault correction they could not handle two machines as would be needed to achieve the cost targets
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Project Behavior Over Time
Discrete System Simulationbull Detailed step-by-step emulation of the flow of entities
through the systemndash With uncertain arrivals processing times andor routing
(branching)bull The computer monitors each simulated entity as simulated
time proceedsndash Enter system move thru according to the various probability
fns governing timing and sequence of eventsbull The computer also records pertinent data regarding the
simulated entities and serversndash wait times throughput queue lengths process times
utilizationhellipbull Creates a synthetic sample of system performance databull Sample data is then analyzed statistically
Types of Problems DSS can Addressbull Performance issues in existing systems
ndash Long waits high inventory poor utilization of resources low throughput
bull Need to estimate performance of a system under designbull What Might One Learn
ndash Where the bottlenecks are and how they might be alleviatedndash How to improve flow reduce queues and wait times and
increase utilization amp throughputndash The optimal number of servers queues buffers etcndash Effective operating rules or policies
Examples of Discrete System Simulationbull Manufacturing facilitiesbull Bank operationsbull Airport operations
(passengers security planes crews baggage)
bull Transportationlogistics distribution operations
bull Hospital facilities (emergency room operating room admissions)
bull Computer networkbull Freeway systembull Business process (eg
insurance office)bull Criminal justice
systembull Chemical plantbull Fast-food restaurantbull Supermarketbull Theme parkbull Emergency Response
system
Project for Systems Science 527 Class Spring 2011
Manufacturing Process Design Drawing
DSS model animation(closely mimics the actual system)
The model contains complex logic regarding A) Different fault occurrences B) Part filling requirements and C) Realistic variations seen in complex assembly processes
Model Resultsbull The simulation showed the behavior of the
proposed new automated systembull It suggested that given expected faults operator
utilization will be 45-55bull Thus if the operator must load parts do audits
and perform fault correction they could not handle two machines as would be needed to achieve the cost targets
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Discrete System Simulationbull Detailed step-by-step emulation of the flow of entities
through the systemndash With uncertain arrivals processing times andor routing
(branching)bull The computer monitors each simulated entity as simulated
time proceedsndash Enter system move thru according to the various probability
fns governing timing and sequence of eventsbull The computer also records pertinent data regarding the
simulated entities and serversndash wait times throughput queue lengths process times
utilizationhellipbull Creates a synthetic sample of system performance databull Sample data is then analyzed statistically
Types of Problems DSS can Addressbull Performance issues in existing systems
ndash Long waits high inventory poor utilization of resources low throughput
bull Need to estimate performance of a system under designbull What Might One Learn
ndash Where the bottlenecks are and how they might be alleviatedndash How to improve flow reduce queues and wait times and
increase utilization amp throughputndash The optimal number of servers queues buffers etcndash Effective operating rules or policies
Examples of Discrete System Simulationbull Manufacturing facilitiesbull Bank operationsbull Airport operations
(passengers security planes crews baggage)
bull Transportationlogistics distribution operations
bull Hospital facilities (emergency room operating room admissions)
bull Computer networkbull Freeway systembull Business process (eg
insurance office)bull Criminal justice
systembull Chemical plantbull Fast-food restaurantbull Supermarketbull Theme parkbull Emergency Response
system
Project for Systems Science 527 Class Spring 2011
Manufacturing Process Design Drawing
DSS model animation(closely mimics the actual system)
The model contains complex logic regarding A) Different fault occurrences B) Part filling requirements and C) Realistic variations seen in complex assembly processes
Model Resultsbull The simulation showed the behavior of the
proposed new automated systembull It suggested that given expected faults operator
utilization will be 45-55bull Thus if the operator must load parts do audits
and perform fault correction they could not handle two machines as would be needed to achieve the cost targets
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Types of Problems DSS can Addressbull Performance issues in existing systems
ndash Long waits high inventory poor utilization of resources low throughput
bull Need to estimate performance of a system under designbull What Might One Learn
ndash Where the bottlenecks are and how they might be alleviatedndash How to improve flow reduce queues and wait times and
increase utilization amp throughputndash The optimal number of servers queues buffers etcndash Effective operating rules or policies
Examples of Discrete System Simulationbull Manufacturing facilitiesbull Bank operationsbull Airport operations
(passengers security planes crews baggage)
bull Transportationlogistics distribution operations
bull Hospital facilities (emergency room operating room admissions)
bull Computer networkbull Freeway systembull Business process (eg
insurance office)bull Criminal justice
systembull Chemical plantbull Fast-food restaurantbull Supermarketbull Theme parkbull Emergency Response
system
Project for Systems Science 527 Class Spring 2011
Manufacturing Process Design Drawing
DSS model animation(closely mimics the actual system)
The model contains complex logic regarding A) Different fault occurrences B) Part filling requirements and C) Realistic variations seen in complex assembly processes
Model Resultsbull The simulation showed the behavior of the
proposed new automated systembull It suggested that given expected faults operator
utilization will be 45-55bull Thus if the operator must load parts do audits
and perform fault correction they could not handle two machines as would be needed to achieve the cost targets
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Examples of Discrete System Simulationbull Manufacturing facilitiesbull Bank operationsbull Airport operations
(passengers security planes crews baggage)
bull Transportationlogistics distribution operations
bull Hospital facilities (emergency room operating room admissions)
bull Computer networkbull Freeway systembull Business process (eg
insurance office)bull Criminal justice
systembull Chemical plantbull Fast-food restaurantbull Supermarketbull Theme parkbull Emergency Response
system
Project for Systems Science 527 Class Spring 2011
Manufacturing Process Design Drawing
DSS model animation(closely mimics the actual system)
The model contains complex logic regarding A) Different fault occurrences B) Part filling requirements and C) Realistic variations seen in complex assembly processes
Model Resultsbull The simulation showed the behavior of the
proposed new automated systembull It suggested that given expected faults operator
utilization will be 45-55bull Thus if the operator must load parts do audits
and perform fault correction they could not handle two machines as would be needed to achieve the cost targets
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Project for Systems Science 527 Class Spring 2011
Manufacturing Process Design Drawing
DSS model animation(closely mimics the actual system)
The model contains complex logic regarding A) Different fault occurrences B) Part filling requirements and C) Realistic variations seen in complex assembly processes
Model Resultsbull The simulation showed the behavior of the
proposed new automated systembull It suggested that given expected faults operator
utilization will be 45-55bull Thus if the operator must load parts do audits
and perform fault correction they could not handle two machines as would be needed to achieve the cost targets
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Manufacturing Process Design Drawing
DSS model animation(closely mimics the actual system)
The model contains complex logic regarding A) Different fault occurrences B) Part filling requirements and C) Realistic variations seen in complex assembly processes
Model Resultsbull The simulation showed the behavior of the
proposed new automated systembull It suggested that given expected faults operator
utilization will be 45-55bull Thus if the operator must load parts do audits
and perform fault correction they could not handle two machines as would be needed to achieve the cost targets
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
DSS model animation(closely mimics the actual system)
The model contains complex logic regarding A) Different fault occurrences B) Part filling requirements and C) Realistic variations seen in complex assembly processes
Model Resultsbull The simulation showed the behavior of the
proposed new automated systembull It suggested that given expected faults operator
utilization will be 45-55bull Thus if the operator must load parts do audits
and perform fault correction they could not handle two machines as would be needed to achieve the cost targets
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Model Resultsbull The simulation showed the behavior of the
proposed new automated systembull It suggested that given expected faults operator
utilization will be 45-55bull Thus if the operator must load parts do audits
and perform fault correction they could not handle two machines as would be needed to achieve the cost targets
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Agent Based Simulation
bull More of a stretch for systems engineering than the othershellip
bull Key Featuresndash Agentsndash Environmentndash Rulesndash Spatial aspectsndash Can reflect heterogeneity of individuals
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Key ABS Conceptsbull Decentralized control
ndash Bottom up as opposed to top down
bull Emergencebull Self-organizationbull Evolutionary considerationsbull Examples
ndash Spread of Forest Firesndash Flockingndash Crowd behaviorndash Ants (and how ants can find optima)ndash Network effects
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Crowd Crush Modelbull The problem crowd panics
bull Sheffield England 1986 96 deadbull Phnom Phen Cambodia November 23 2010
347 deadbull Duisburg Germany July 25 201019 dead
bull This model was developed as class projectbull Alexandra Nielsen Systems Science 525 Fall 2010
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Crowd Crush defined Die of asphyxiation not blunt force trauma
Can die standing Warning of a crush
Surrounded on all sides More than 4 people per square meter
Force to kill The force of 5 people pushing on one person
can break a rib collapse a lung smash a childs head
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Simulation purpose Discover why some crowds are lethal and others not
Can crowd deaths occur in non-aggressive crowds Does aggression or reactivity (jostling) have a greater impact on
crowd deaths Is there some combination of factors that is reliably lethal (So we
can avoid it) What interventions can prevent deaths Is there a critical density after which nothing will work
Netlogo modelhellip
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Model Testing Interventions
Opening closing entrance exit Shortening corridor (simulate smaller crowd) Panic on seeing another dead
Validate vs anecdotal evidence Wal-Mart door rush Cambodia see a ldquobodyrdquo rarr panic Opening door in a crowd rarr death (Barnsley
Public Hall disaster)
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Applicability and Limitations Large crowds single doorway
Love Fest One entrance of a soccer stadium (Liverpool) Good for understanding simple crowd
dynamics Limited by simplifying assumptions (extremely
simple) No falling Only forward motion No groups altruism variability in agents Forces not vectors not true physical force
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
Findings
bull Dont allow a huge build up then open a doorbull Closing the gates before clearing the corridor
helps but not muchbull Do anything you can to prevent panic
