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Agent Based Models for Enterprise Wide Optimization and Decision SupportOptimization and Decision Support
Raj SrinivasanRaj SrinivasanDept of Chemical & Biomolecular Engg
National University of SingaporeProcess Systems & Modeling
Institute of Chemical & Engg Sciencesy g p gg
CMU, 1 Dec 20091
Process Systems Engineering• Chemical cluster design & optimization
• Enterprise-wide Opt & DS
CEO• Agent-based modeling • Dynamic simulation• Disruption management
Enterprise wide Opt & DS
Supply Chain
Enterprise
• Refinery (re)scheduling
• Disruption management
Planning / Scheduling
Supply Chain
• Inherent safety / Sustainability studies
• Schedule robustness metrics
Process Supervision
Process Optimization
S ft / l t
studies• Waste minimization / Energy /
Water optimization
Unit Control
p• Soft sensors / alarm mgmt• Process transitions mgmt• Fault diagnosis• Fault tolerant control
Plant Operator• Image based control of particulate processes
2
A Energy Company
Electricity
R fi i
Jet Fuel
Oil & Gas Transportation
Refining
Productionp
Storage & Transportation
Petrol & Diesel
Petrochemicals
3
Supply Chain Management
Crude Oil Operations Scheduling
4
All for the want of a nail…“For want of a nail, the shoe was lost,For want of a shoe, the horse was lost,For want of a shoe, the horse was lost,For want of a horse, the rider was lost,For want of a rider, a message was lost,, g ,For want of a message, the battle was lost,For want of a battle, the kingdom was lost,And all for the want of a nail!”
George Herbert, in Outlandish Proverbs (1640)
5
Integarted Models of Supply Chains & Enterprises
6
PSE 101: Unit Operations
Unit-leveld li d i i l ti t l ti i timodeling, design, simulation, control, optimization
“Physicochemical phenomena”Physicochemical phenomena Virtual units through Objects / Aspects 7
PSE 102: Process PlantsPlant-wide
design, simulation, controlsynthesis planning schedulingsynthesis, planning, schedulingsupervision, maintenance, risk
“Network of units” 8
From PSE to PSE2
PSE2 101: Entities & FacilitiesEnterpriseSuppliers Customers
LSPsLSPs
“Network of Networks”9
Process Flow Diagrams
Methanol product
Plant-widedesign, simulation, control
synthesis planning schedulingsynthesis, planning, scheduling,supervision, maintenance, risk mgmt
10
Supply Chain
Raw material sourcing Primary production Secondary production Warehouses RetailersCustomers
11
Refinery Supply ChainLogistics
Provider CRefinery BLogistics
NaphthaBrent Crude Provider A
Ethylene
Refinery CLogistics LPG
Chemicals Manufacturer D
Arab CrudeyLogistics
Provider B
Logistics
LPG
Crude Producer
Provider D
Information Flow Material Flow MaterialInformation Flow Material Flow Material
12
Supply Chain Management
• Fundamental Questions– What product (mix) to sell?p ( )– What raw materials are needed and when should they be bought?
• Operate Drivers• Operate – Demand forecasting– Scheduling & planning
Drivers Optimize / manage logistics,
inventories, other supply chain resources
• Design– Facility / network planningFacility / network planning– Transportation network design
13
Business Processes of a RefineryTypical Supply Chain
Sales Operations
StStorage Procurement Logistics14
Crude Procurement Process
Posting PostingPosting
EXCHANGE
1. Procurement initiation
2. Market dataFetch quotes from postings on
the exchange
LEGEND
...............
Posting gPosting
3. Crude basket
5. List of pickup location
6. Request for bids
OPERATIONS
PROCUREMENT
4. Refined crude basketand pickup date for
each crude
7. Bids received from 3PLs
CBA
OPERATIONS
STORAGE
31 2
AND
OR
Bid deadline over
8. List of best bids foreach crude9. Place order for
crude
10. Orderconfirmed
SALES
11. Information on crudebought
12. Contractawarded to
respective 3PL13. Order
Confirmation14. TransportInformation
15. TransportInformation
LOGISTICS
CBA3PL
OIL SUPPLIER
15
Agent based Models of Supply Chains & Enterprises
16
What is an Agent?An computational entity
– Perceives its environment Element in a MA System• MAS: Loosely coupled
(sensors)– Acts upon it (actuators)– Is autonomous
MAS: Loosely coupled network of agents– Collectively capable of solving
problemsIs autonomous– Pursues goals or carries out
tasks to meet objectivesProactively or reactively
problems– Achieve goals beyond an
individual agent – Proactively or reactively – Relying on Social-ability
• Heterogeneous agents• Interaction Coordination
Cooperation Competition
Planning Negotiationg
Distributed Centralized
g
17
Agents in Refinery Supply Chain
Refinery Departments
PostingPostingPosting
PETROLEUMEXCHANGE
PROCUREMENT
...............
PROCUREMENT
31 2STORAGESALES
OIL SUPPLIERS
LOGISTICS 3PLsCBA
LOGISTICSOPERATIONS3PLs
18
Agent Behavior
Agent: Time or StorageMessage: Start Procurement Cycle
Agent: Sales
Agent: SalesMessage: Request Forecast
Agent: SupplierMessage: Inform Forecast
Agent: SupplierMessage: Inform Available Crude
Message: Query Available Crude
Agent: OperationsMessage: Refine Crude Basket
Agent: OperationsMessage: Refined Crude Basket
Agent: Logistics
Agent: LogisticsMessage: Request 3PL to Bid
Agent: Supplier
PROCUREMENT
Agent: LogisticsMessage: Best Bid from 3PL
Agent: SupplierMessage: Confirmation of Purchase
Agent: SupplierMessage: Send Purchase Order
Agent: LogisticsMessage: Confirmation of PurchasePrimary function: Purchase crude
Agent: LogisticsMessage: Compiled Transport Info
Agent: StorageMessage: Inform Transport Details
Plan: <plan name="procure crude">
Primary function: Purchase crudeGoal: Purchase at fair price, whenever required
Plan: <plan name= procure_crude ><body>new CrudeProcurementPlan()</body><trigger> <messageevent ref="start_procurement_cycle"/> </trigger>
</plan>
19
Agent Interactions
20
Agent Interactions
• Message passing to emulateemulate– Information flow– Material flowMaterial flow
• Communication between agents resultsbetween agents results in ‘discovery’ of supply chain structure’ – Structure established
dynamically not pre-specifiedspecified
21
Supply Chain Flow Diagram
22
Supply Chain Flow Diagram
23
System Flow Diagrams
Methanol product
Raw material sourcing Primary production Secondary production Warehouses RetailersCustomersa ate a sou c g y p y Warehouses Retailers
24
Simulation Studies
1500
Crude 1 Crude 2 Crude 3 Crude 4 Crude 5
1000
Vol
ume
(kbb
l)
500Cru
de V
000 14 28 42 56 70 84 98 112
Time (days) 25
Throughput – Actual vs. Planned
220
200
y)
Actual Planned
160
180
hput
(kbb
l/day
140
160
CD
U T
hrou
gh
120
1000 14 28 42 56 70 84 98 112
Time (days)26
Decision SupportDecision Support
New policies: Planning, Scheduling, Tactical decisions: Tank conversionTactical decisions: Tank conversion
Strategic decisions: Capacity expansion
27
Effect of Policies on Supply Chain Performance
KPI Base Case New ProcurementPolicy
New ProductionPolicy
Average Crude InventoryAverage Crude Inventory (kbbl) 2232.5 1866.6 2036.8
Average product Inventory (kbbl) 1976.9 1953.8 1417.9 (-28%)y ( )
Average CDU throughput (kbbl) 145.5 144.9 135.0 (-7%)
Product revenue 1065.0 1065.0 1065.0Crude Procurement Cost 942.6 908.8 (-4%) 854.4
Crude Inventory Cost 13.4 11.2 (-16%) 12.2P d t I t C t 11 9 11 7 8 5 ( 28%)Product Inventory Cost 11.9 11.7 8.5 (-28%)
Operating Cost 35.1 35.0 32.59Product deficit Penalty 0.0 0.0 0.0
Demurrage cost 14.5 0.0 (-100%) 0.0Profit (Million$) 47.76 98.51 (+106%) 157.30 (+60%)
Strategic Decision MakingGasoline
600
800
1000
nd (k
bbl)
Jet Fuel
200
300
nd (k
bbl)
Product demands expected to go up
0
200
400
0 120 240 360 480Time (days)
Fore
cast
Dem
an
0
100
0 120 240 360 480Time (days)
Fore
cast
Dem
an
expected to go up
CDU Expansion? YesTime (days)
Diesel
400
600
800
man
d (k
bbl)
Time (days)
Fuel Oil
200
400m
and
(kbb
l)Yes
0
200
400
0 120 240 360 480Time (days)
Fore
cast
Dem
0
200
0 120 240 360 480Time (days)
Fore
cast
Dem
350
400
200
250
300
350
U T
hrou
ghpu
t (kb
bl/d
ay
Investment $100 millionOpportunity: $ 249 7 million
100
150
200
0 120 240 360 480Time (days)
CDU
Before CDU capacity expansionAfter CDU capacity expansion
Opportunity: $ 249.7 millionPayback period: 0.44 year 29
New Types of DecisionsNew Types of Decisions
Risk Identification & ManagementDisruption: Emergency procurement,
Domino effectsDomino effectsOthers: Quantify risk, Supplier contracts
30
Supply Chain Management
• Fundamental Questions– What product (mix) to sell?p ( )– What raw materials are needed and when should they be bought?
• Operate Demand forecasting
Drivers O i i / l i i– Demand forecasting
– Scheduling & planning– Risk management
Disruption management
Optimize / manage logistics, inventories, other supply chain resources
Take advantage of market opportunities– Disruption management• Design
– Facility / network planning
Take advantage of market opportunities in product demands, raw material availabilities
React efficiently to disruptions and– Transportation network design– Risk management
React efficiently to disruptions and other supply, production, or demand uncertainties
31
Supply chains are VulnerableRisk drivers:• Global operations• Single sourcingg g• Specialized
production sites• Outsourcing
L l h i• Lean supply chain• Just-in-time
McKinsey survey (2006):• Two-thirds: risks have increased over theTwo thirds: risks have increased over the
past five years• 41%: company doesn’t spend enough
time or resources on mitigating risk• 25% no formal risk assessment• Almost half lack company-wide
standards to help mitigate risk32
Supply Chain Flow Diagram
33
ESupply Chain HAZOP Analysis
• Deviation:• No crude arrival
No + Crude arrival
• Causes• Jetty unavailability• Shipper disruption
arrival
• Supplier stock-out• Consequences
• Low stock, out-of-crudeO ti di t d• Operation disrupted
• Demand unfulfilled• Safeguards
• Safety stock• Safety stock• Mitigating Actions
• More reliable shipper34
Consequence Analysis via Simulation
3PL ReliabilityAverage Customer Satisfaction (%)
i h3PL Reliability
Average Profit ($, million)
High Low
Yes 98 95
No 95 91
Safety stock
High Low
Yes 93 38
No 83 27Safety stockNo 95 91
• Current safety stock cannot make up for poor performance of 3PL provider • Additional safeguards or mitigating actions required e g higher safety
No 83 27stoc
• Additional safeguards or mitigating actions required, e.g. higher safety stocks, emergency crude procurement.
• Probability of crude arrival delay: High 3PL reliability (0 05) Low 3PL reliability (0 10)• Probability of crude arrival delay: High 3PL reliability (0.05) – Low 3PL reliability (0.10)• Safety stock: crude (100 kbbl) – product (20%)• # Simulation runs per scenario: 300• Demand variability across cycles: 25%
Si l ti h i 120 d• Simulation horizon: 120 days
35
Process Control & Supervision
Controller Malfunction
Feedback Controller
Sensor FailureProcess DisturbanceH E
Dynamic ProcessActuator Sensorsu y
Sensor FailureProcess DisturbanceHuman Error
Actuator Faults Structural Failures
Diagnostic System
36
Disruptions in Crude Procurement Process
PostingP ti
EXCHANGE
1. Procurement initiation
2. Market dataFetch quotes from postings on
the exchange
LEGEND
...............
Posting PostingPosting
3. Crude basket
5. List of pickup location
6. Request for bids
OPERATIONS
PROCUREMENT
4. Refined crude basket
p pand pickup date for
each crude
7. Bids received from 3PLs
CBA
OPERATIONS
STORAGE
31 2
AND
OR
7. Bids received from 3PLs
Bid deadline over
8. List of best bids foreach crude9. Place order for
crude
10. Orderconfirmed
SALES
11. Information on crudebought
12. Contractawarded to
respective 3PL13. Order
Confirmation14. TransportInformation
15. TransportInformation
LOGISTICS
CBA3PL
OIL SUPPLIER
37
Supply Chain DashboardA DCS for the Supply Chain######
Customer Satisfaction 100
95 98 100
40
60
80
100
Perc
enta
ge
Revenue from Sales
288
241 238
298
150
200
250
300
350
ales
(K$)
Operating Cost
245 230 243
298
150
200
250
300
350
atin
g C
ost (
K$)
pp y
Brent Crude GasolineRaw Materials Shipment Actual Demand Forecast DemandM d Di l
0
20
Mar '08 Apr '08 May '08 Jun '08
P
0
50
100
Mar '08 Apr '08 May '08 Jun '08
Sa
0
50
100
Mar '08 Apr '08 May '08 Jun '08
Ope
ra
800 800Raw Materials Shipment Actual Demand Forecast Demand Arrival Date Type Status Due 24 Jul 08 Due 31-Jul-08
1 27-Jul-08 B & O & A On time Gasoline 250 Gasoline 2802 3-Aug-08 B & K On time CDU Throughput Jet Fuel 63 Jet Fuel 953 9-Aug-08 B & O & A Scheduled Diesel 125 Diesel 1254 16-Aug-08 B & O & A Scheduled Kuwait Crude Total throughput 200 Jet Fuel Fuel Oil 122 Fuel Oil 154
Brent 90Kuwait 110 Products ShipmentD b i 0 Shi t D t T St t
Mode Diesel
0
400
800
800
1200 400
0
400
800
Dubai 0 Shipment Date Type StatusOman 0 1 G + D WIPArab Light 0 2 J + F WIP
Dubai Crude Diesel
24-Jul-0824-Jul-080
400
0
400
800
1200
0
400
800
0
Oman Crude Fuel Oil
Arab Light Crude
0
0
400
800
1200
0
400
0
g
Today's Date
20-Jul-08 0
400
800
38
Disruption Management
Supply ChainKPI
Corrective A ti
2000
2500
100
150
200
250
300
350
1
4
7
KPIs Actions
Monitoring of KPIs
100
200
300
400
500
1000
1500
0
500
1000
1500
2000
0 10 20 30 40
0
50
100
150
200
250
300
0
50
100
0 10 20 30 40 50 60 70 80 90 100Seeking Optimal Rectification:
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
0
0 10 20 30 40
0
0 20 40 60 80 100
Seeking
Rectification optionsAlarms
Root Cause Diagnosis
Seeking Rectification Actions
Diagnosis
Disruption Details 39
Scenario 1: Transportation DelayInventory Profile
200025003000 • Ship’s scheduled date of arrival:
115
0500
10001500
0 20 40 60 80 100 120
• Ship’s delayed date of arrival: 121
• Amt of Crude on Board: 1560Throughput Details
200
250
300
350 Stock-out for 2 days
• Amt of Crude on Board: 1560 kbbl
• Disruption detected on: 112 (f i f
0
50
100
150
200
0 20 40 60 80 100 120
(from inventory, future shipments, throughput KPIs)
• Product to be delivered on: 120Product Inventory
200250
300350
• Date of Stock-out: 119• Amt of crude Shortfall: 758 kbbl
050
100150
200
0 20 40 60 80 100 120 140
Unchecked DisruptionDisruption Managed
40
Scenario 1: Transportation Delay
Can’t unload into Tank 6
Parcel 7 delay from Day 115 to 121
as it is charging CDU 3
-30 U3-50 U3-50 U3100 P7100 P7
Tank 6 will run out of crude at time 11
Existing schedule is infeasible!41
All for the want of a nail…“For want of a nail, the shoe was lost,For want of a shoe, the horse was lost,For want of a shoe, the horse was lost,For want of a horse, the rider was lost,For want of a rider, a message was lost,, g ,For want of a message, the battle was lost,For want of a battle, the kingdom was lost,And all for the want of a nail!”
George Herbert, in Outlandish Proverbs (1640)
42
Spec Chem Mfg EnterpriseRefinery
B Oil
ZDTPRefinery
Isotanker
Supplier
Base Oil
Lube Additive Plant SulfonatesDepot
Drums
ppMMA
Dispersants
Reaction
Blending
Lube Additive Package
Packaging
Others
Consumer
Supplier(ethyleneamines, etc) Plant
Plant 1 Plant 2 Plant 3Plant 1
HQ Customer
Plant 2 Plant 3
43
Enterprise Model
C tRaw Material &
il liCustomers
Central Sales
Base Oil Suppliers
Department
Plant 1 Plant 2 Plant 3Plant 1 Plant 2 Plant 3
•Scheduling •Operation•PackagingSt
•Scheduling •Operation•Packaging
•Scheduling •Operation•Packaging
•Storage•Procurement•Economics•Logistics
•Storage•Procurement•Economics•Logistics
•Storage•Procurement•Economics•Logistics
44
Enterprise-level Coordination
2
13
4
6
5
Scheduler
Operations
P k i
Storage
Procurement
E iPackaging
Logistics
Economics
45
Integrated Enterprise Model – ILAS
46
Case Studies
• CS 1 & 2: Job assignment policy• CS 3: Reassignment during plant disruption• CS 4: Scheduling policyg p y• CS 5: Procurement policy• CS 6: Strategic decision• CS 6: Strategic decision• CS 7: Dealing with unreliable 3PL
47
Case Study 1: Job Assignment Policyy
Equal Assignment
Expected Completion
Date
13 69 15 68 + 15%
• Base case: Equal Assignment» Orders are assigned to 3 plants equally
• New policy: Expected Completion Date
μ ± σ
Overall Profit (M$)13.69 15.68
± 0.86 ± 0.87
Overall Customer Satisfaction
66% 85%
± 12% ± 14%
15%
+ 29%
• New policy: Expected Completion Date» Orders are assigned to plant which can
deliver at the earliest• All orders are accepted, no matter how
Overall Plant Utilization
89% 90%
± 3% ± 4%
Total Tardiness (days)
294.53 75.62
± 178.58 ± 99.16
- 74%
plate the delivery will be
» for fair comparison
Expected Completion Date results in:• higher profit• higher customer satisfaction and lower tardiness Coordination between HQ and plants improves overall performance.
Demand 1x; 360 days simulation; 100 simulation runs; ± 250 orders48
Case Study 2: Job Assignment Policy (cont.)( )
+ 2.3%
• Another policy: Customer Location» Assign order to the plant nearest to the
customer if it can meet due date
Expected Completion
Date
Customer Location
O ll P fit (M$)9.01 9.22
μ ± σ
- 4%
» If it can’t, try the next nearest plant» If no plant can meet order’s due date, the
order will not be taken (i.e. missed order)
Overall Profit (M$)± 0.88 ± 0.90
Delivery Cost (M$)1.98 1.71
± 0.13 ± 0.09
O C 93% 89%
- 13%
- 4%• vs. Expected Completion Date» with missed order allowed
Overall Customer Satisfaction
93% 89%
± 3% ± 4%
Overall Plant Utilization
98% 97%
± 1% ± 1%
Customer Location based job assignment results in:• lower customer satisfaction, higher tardiness
» doesn't always assign to the earliest plant» less buffer from due date
Total Tardiness (days)
2.88 4.47
± 1.26 ± 1.86
No of Missed Orders
26.59 27.01
± 4 21 ± 4 15» less buffer from due date• higher profit
» transportation savings more than offset late penalties
± 4.21 ± 4.15
Demand 1.3x; 180 days simulation; 100 simulation runs; ± 125 orders49
Case Study 3: Reassignment during Plant Disruption
• No reassignment» job-in-progress will be restarted
when the plant is back up and jobs
No Disruption
No Reassignment
With Reassignment
Overall Profit (M$)
7.77 7.60 7.58
± 0 68 ± 0 66 ± 0 67
μ ± σ
in the schedule follow• Job Reassignment policy
» job-in-progress and jobs in the schedule of disrupted plant is sent
( $) ± 0.68 ± 0.66 ± 0.67
Overall Customer Satisfaction
99% 98% 99%
± 1% ± 2% ± 1%
Singapore Plant ( )
0.25 37.33 0.14schedule of disrupted plant is sent back to Sales
» Sales reassigns these jobs to the other 2 plants
Tardiness (days) ± 0.66) ± 29.2 ± 0.40
Houston Plant Tardiness (days)
0.21 0.39 0.60
± 0.50 ± 0.80) ± 1.02
Japan Plant 0.18 0.29 0.32• Singapore plant disruption from day Japan Plant Tardiness (days)
0 8 0 9 0 3
± 0.48 ± 0.84 ± 0.94
Overall Plant Utilization
85% 84% 83%
± 5% ± 4% ± 4%
• Singapore plant disruption from day 75-100
• With no reassignment» High tardiness for Singapore plant
No of Missed Orders
1.27 2.69 3.16
± 1.23 ± 2.06 ± 2.21
» High tardiness for Singapore plant due to disruption
Job Reassignment results in:» Tardiness of Singapore plant significantly decreased
Demand 1.2x, 180 days simulation; 100 simulation runs; ± 125 orders
» Tardiness of Singapore plant significantly decreased» with very small increase in tardiness of the other 2 plants» and slightly more missed order 50
Case Study 4: Scheduling PolicyPEDD PEDD-LJS
Overall Profit (M$)19.14 19.37
± 1.19 ± 1.07
• PEDD (Processing Earliest Due Date)New Job
Job ID: 13PEDD: Day 22
μ ± σ
Overall Customer Satisfaction
65% 92%
± 4% ± 2%
Overall Plant Utilization
97% 97%
± 1% ± 1%
PEDD: Day 22
Utilization ± 1% ± 1%
No of Missed Orders
54.16 56.88
± 5.76 ± 5.93New Job
Job ID: 13PEDD: Day 22
• PEDD with Late Jobs Consideration
PEDD i h L J b C id i l i
If the insertion of the new order causes List No 2 to be late, the new job will be placed one level lower so as to avoid a late job
PEDD with Late Jobs Consideration results in:• significantly improved customer satisfaction (0.92, up from 0.65)• slightly more missed order
» PEDD-LJS completion date is later or at most same
Demand 1.3x; 360 days simulation; 100 simulation runs; ± 250 orders
» PEDD-LJS completion date is later or at most sameA small modification to a policy could have a big impact.
51
Conclusions
• Dynamics are importantSh t t & l t– Short-term & long-term
– Decisions related to operation, control & design
• Agent based models offer a natural paradigm for• Agent based models offer a natural paradigm for modeling the enterprise– Simulation-optimization strategy for designSimulation optimization strategy for design– Control structure for disruption management
• From PSE to PSE2 (= PSE of Enterprise)o S o S ( S o e p se)– Analogy from PSE are useful
• Representation, Modeling & simulation • Control & supervision
52