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Presentation at TRB 90th Annual Meeting Yard Crane Scheduling at Seaport Container Terminals: A Comparative Study of Centralized and Decentralized Approaches by Omor Sharif and Nathan Huynh University of South Carolina - PowerPoint PPT Presentation
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Presentation at TRB 90th Annual Meeting
Yard Crane Scheduling at Seaport Container Terminals: A Comparative Study of
Centralized and Decentralized Approaches
by
Omor Sharif and Nathan HuynhUniversity of South Carolina
Presented at the Joint Meeting of the Ports and Channels Committee (AW010) and the Intermodal Freight Terminal Design and
Operations Committee (AT050)
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OutlineWhat is Yard Crane Scheduling Problem?
Review of Centralized Solution
Review of Decentralized Solution
Design of Experiments and Results
Comparative Performance between the two approaches
Conclusion/Future Directions
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Yard Crane Scheduling Problem
Objective: Determining best sequence of trucks to serve by each yard crane.
Challenges:There are fluctuations in truck arrivalJob locations are distributed throughout the yard zoneGood decisions are difficult to conceive manually
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Yard Crane Scheduling (YCS) Problem
Operational improvement of container terminal
Reducing drayage trucks turn time
Efficient allocation of scarce resources
Environmental Concerns
Motivation
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Solution to YCS Problem
Centralized Approaches
-OR Optimization- IP
- MIP
Decentralized Approaches
- Agent-based Modeling
YCS Problem Solution
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Research Questions
Comparative Study between the two approaches
Contrasting assumptions?
Strengths and weaknesses?
Relative performances?
Suitability for implementation?
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Centralized Approach
Based on the work of Ng (2005)
IP was developed for optimal crane scheduling
Considers multiple yard cranes and known arrival times
Excessive computational time required to solve IP
Dynamic programming based heuristic is proposed
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Centralized ApproachHow the Heuristic solves YCS?
Heuristic has TWO phases
First Phase (Find Best Partition) • Partitioning of the Yard Zone• Several smaller groups equal to number of
YCs• Job handling follows greedy heuristic• Output is best partition with least total
waiting
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Centralized ApproachHow the Heuristic solves YCS?
Heuristic has TWO phases
Second Phase (Job Reassignment)
• Job reassignment between adjacent YCs• Interference check required• Algorithm considers two cranes at some
time• Output is the minimum total waiting found
by heuristic
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Centralized ApproachA Sample Heuristic Solution
First PhaseSolution
Second PhaseSolution
Path of the Cranes
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Decentralized ApproachDistributed perspective in recent years
Based on the work of Huynh and Vidal (2010)
Agent based approach
Each YC is an agent seeking to maximize utility
Decisions are based on the valuation of utility function
Utility functions are designed to minimize waiting time
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Decentralized ApproachUtility Functions
Distance Based Utility
Time Based Utility
D = Distance to TruckT = Truck Wait Timep1 and p2 = Penalty Values (discouraging penalties)Xinterference, Xproximity, Xturn and Xheading are binary variables
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Decentralized Approach
Simulation model, coded in Netlogo Netlogo: A multi-agent programmable Environment
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Key DifferencesCentralized approach
Decentralized approach
Optimization strategy
Global optimization.
Agent based local optimization.
Work flow Optimal schedule.
Individual decisions.
Arrival information
Assumes complete information.
No assumption.
Truck sequencing
Greedy approach Cranes’ utility functions.
Implement-ation Dynamic
heuristics.Agent-based simulation.
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Experimental DesignA large set of YCS problems were solved
Experiment Set 1: Impact of Number of Yard CranesNumber of YCs ⟶ 2 to 4Experiment Set 2: Impact of Truck Arrival RateNumber of Jobs ⟶ 5, 10 and 15Experiment Set 3: Impact of Yard SizeNumber of Yard blocks ⟶ 1 to 3Experiment Set 4: Impact of Truck VolumeNumber of Jobs ⟶ 20, 50 and 80Job location distribution ⟶ Random Uniform DistributionJob arrival distribution ⟶ Poisson Distribution
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Comparative Performance between the two approachesOptimality - Minimize the truck waiting timeCentralized Approach• Heuristic produces near-optimal schedule• On average 7.3% above the lower bound
Decentralized Approach• No advance schedule for the agents• On average 16.5% above the heuristic
solution
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Comparative Performance between the two approachesOptimality - Minimize the truck waiting time
Fig: Mean Index for different truck arrival rates
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Comparative Performance between the two approachesOptimality - Minimize the truck waiting time Fig:
Mean Index for different yard sizesFig: Mean Index for different job volumes
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Comparative Performance between the two approachesScalability and computational efficiency
Centralized Approach• Highly sensitive to the size and complexity• Requires performing the computation in
advance
Decentralized Approach• No computation time required in advance• Disaggregated, handle large and complex
problems
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Comparative Performance between the two approachesAdaptability
Centralized Approach• Assumes complete information on supply
and demand• Requires rescheduling to adapt with changes
Decentralized Approach• No assumptions on the arrival-time of trucks• Monitor changes continuously, adapt rapidly
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Concluding Remarks/ Future Work
Two approaches have complimentary solution propertiesHybrid approaches may offer better resultsProposed Hybrid Approach I• Local optimization models for cranes• Coordination for best partition within yard
zoneProposed Hybrid Approach II• Solve global optimization periodically• Switch to adaptive agent-based model when
necessary
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Thank You
Questions ?