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Thesis Defense Investigation of Agent-Based Approaches to Enhance Container Terminal Operations by Omor Sharif Presented in Partial Fulfillment of the Requirements For the Degree of Master of Science in Civil Engineering 2011. What is a Container Terminal (CT)?. - PowerPoint PPT Presentation
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THESIS DEFENSE
Investigation of Agent-Based Approaches to Enhance
Container Terminal Operations
byOmor Sharif
Presented in Partial Fulfillment of the Requirements
For the Degree of Master of Science inCivil Engineering
2011
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What is a Container Terminal (CT)?
An interface between ocean and landShips are loaded and unloadedContainers are temporarily storedManage handling of Containers etc
Flow of Containers and Decision Problems
BERTH ALLOCATION
QUAY CRANE SCHEDULING
TRANSPORT OF CONTAINERS TO STORAGE AREA AND VICE VERSA
YARD OPERATIONS - STORAGE SPACE ASSIGNMENT
YARD OPERATIONS – YARD CRANE SCHEDULING
DELIVERY AND RECEIPT OPERATIONS (GATE OPERATIONS)
CUSTOMER/DEPOTS
TERMINALGATE
STORAGEAREA
SHORE/BERTH
VESSEL/ SHIP
XTs XTs AGVs/ITs/SCs QCsYCs
ARRIVAL AND STORAGE RETRIEVAL AND LOADINGRETRIEVAL AND PICKUP UNLOADING AND STORAGE
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Two Research Studies
Yard Crane Scheduling Problem
Truck Queuing at Terminal
Gates
Research Topics
1. Sharif, O., Huynh H. (2011) “Yard crane scheduling at seaport container terminals: A comparative study of centralized and decentralized approaches”. Paper to be submitted for publication.2. Sharif, O., Huynh, H., Vidal, J. (2011) “Application of El Farol model for managing marine terminal gate congestion”. Submitted to Journal of Research in Transportation Economics.
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Journal Article I
Yard Crane Scheduling at Seaport Container Terminals: A Comparative Study of
Centralized and Decentralized Approaches
by
Omor Sharif and Nathan HuynhUniversity of South Carolina
Paper to be submitted for publication
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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 RateArrival Rate ⟶ 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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Journal Article II
Application of El Farol Model for Managing Marine Terminal Gate
Congestion
by
Omor Sharif , Nathan Huynh and Jose VidalUniversity of South Carolina
Submitted to Journal of Research in Transportation Economics
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OutlineGate Congestion problem at CT
Proposed Model and Implementation
Design of Experiments and Results
Concluding Remarks
Congestion Problem at Terminal Gates
Documentation processing, inspection, security checks etcLong waiting time due large number of idling trucksImpact turn around time of drayage trucksEnvironmental concern due to significant emission
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Attempted Solution
Appointment Systems/
Reservation Systems with Time
Windows
Real Time Gate Congestion
Information Using Webcams
Solution to the Gate Congestion Problem
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Proposed Agent-based Model
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Proposed Agent-based Model (Contd.) N ≡ Set of Depots (n ∈ N)T ≡ Set of Trucks (t ∈ T)L ≡ Tolerance (Max allowed waiting time)E (W) ≡ Expected waitSEND? (n, t) ≡ 1 if E (W) ≤ L 0 otherwiseTotal time before entry into port = T (n, P) + Q(t) + S(t)Wait at gate, W(t) = Q(t) + S(t)I ≡ Discretization intervalAverage waiting at xth interval, Historyx = { }
x
x,t
C
)x,t(W)x,t(W
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Proposed Agent-based Model (Contd.) Parameters related to ‘Predictors’S = [s1, s2 ,s3 ,..., sz] ≡ Predictor space containing z predictorsk ≡ Number of predictors chosen from Smy-predictors-list(n) ≡ Predictor set of depot agent nmy-predictors-scores(n) ≡ Rank of predictors of depot agent nmy-predictors-estimates(n) ≡ for each predictorsactive−predictor(n) ≡ Best performing predictor for depot agent n Updating of scoresOriginal Precision Approach: is a number strictly between zero and one
Proposed Agent-based Model (Contd.)
Pseudo Code of the Program – Part of the Main Loop
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Model Implementation
Simulation model, coded in Netlogo
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Experimental DesignParameter Value UnitNumber of Depots 10 NosDispatch rate (θ) 12 trucks/
depot/hrMean transaction time (μ)
3, 4, 5, 6, 7 and 8
minutes
Tolerance (L) 15, 20, 25 and 30
minutes
Total predictors 200 NosPredictors per depot (k)
12 Nos / depot
Update interval (I) 5,10 and 15 minutesMaximum memory (m)
20 intervals
Predictor scoring policy
Original precision
n/a
Alpha 0.5 n/a
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Results (Mean wait and Total completion)Fig: Impact of tolerance on mean wait time of trucks
Fig: Impact of tolerance on total completion time.
Results (Mean wait time history)Fig: Mean wait time of trucks (I =15 minutes, L = 15 minutes)
Fig: Mean wait time of trucks (I =10 minutes, L = 10 minutes)
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Results (Base Case Comparison)
43% and 63% lower mean wait time for I = 5 and 10 mins22% and 40% lower maximum wait time for I = 5 and 10 mins18% and 40% higher completion time for I = 5 and 10 mins
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Concluding RemarksProposed model provides steady truck arrival
Adopt higher ‘I’ for distributing demand
Good amount of emission reduction over ‘do-nothing’
First study of its kind
Additional studies are required to understand complexity
More sophisticated learning models
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Thank You
Questions ?
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