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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)?.
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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)? • An interface between ocean and land • Ships are loaded and unloaded • Containers are temporarily stored • Manage handling of Containers etc
Berth Allocation Flow of Containers and Decision Problems • Quay Crane Scheduling • Transport of Containers to Storage Areaand Vice Versa • Yard Operations - Storage Space Assignment • Yard Operations – Yard Crane Scheduling • Deliveryand Receipt Operations (Gate Operations)
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.
Journal Article I 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 Paper to be submitted for publication
Outline • What 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
Yard Crane Scheduling Problem • Objective: Determining best sequence of trucks to serve by each yard crane. • Challenges: • There are fluctuations in truck arrival • Job locations are distributed throughout the yard zone • Good decisions are difficult to conceive manually
Yard Crane Scheduling (YCS) Problem Motivation • Operational improvement of container terminal • Reducing drayage trucks turn time • Efficient allocation of scarce resources • Environmental Concerns
Research Questions • Comparative Study between the two approaches • Contrasting assumptions? • Strengths and weaknesses? • Relative performances? • Suitability for implementation?
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
Centralized Approach How the Heuristic solves YCS? Heuristic has TWO phases
Centralized Approach How the Heuristic solves YCS? Heuristic has TWO phases
Centralized Approach A Sample Heuristic Solution First Phase Solution Second Phase Solution Path of the Cranes
Decentralized Approach • Distributed 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
Decentralized Approach Utility Functions D = Distance to Truck T = Truck Wait Time p1 and p2 = Penalty Values (discouraging penalties) Xinterference, Xproximity, Xturn and Xheading are binary variables
Decentralized Approach • Simulation model, coded in Netlogo • Netlogo: A multi-agent programmable Environment
Experimental Design • A large set of YCS problems were solved • Experiment Set 1: Impact of Number of Yard Cranes • Number of YCs ⟶ 2 to 4 • Experiment Set 2: Impact of Truck Arrival Rate • Arrival Rate ⟶ 5, 10 and 15 • Experiment Set 3: Impact of Yard Size • Number of Yard blocks ⟶ 1 to 3 • Experiment Set 4: Impact of Truck Volume • Number of Jobs ⟶ 20, 50 and 80 • Job location distribution ⟶ Random Uniform Distribution • Job arrival distribution ⟶ Poisson Distribution
Comparative Performance between the two approaches Optimality - Minimize the truck waiting time
Comparative Performance between the two approaches Optimality - Minimize the truck waiting time Fig: Mean Index for different truck arrival rates
Comparative Performance between the two approaches Optimality - Minimize the truck waiting time Fig: Mean Index for different yard sizes Fig: Mean Index for different job volumes
Comparative Performance between the two approaches Scalability and computational efficiency
Comparative Performance between the two approaches Adaptability
Concluding Remarks/ Future Work • Two approaches have complimentary solution properties • Hybrid approaches may offer better results
Journal Article II Application of El Farol Model for Managing Marine Terminal Gate Congestion by Omor Sharif , Nathan Huynh and Jose Vidal University of South Carolina Submitted to Journal of Research in Transportation Economics
Outline • Gate 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 etc • Long waiting time due large number of idling trucks • Impact turn around time of drayage trucks • Environmental concern due to significant emission
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 wait SEND? (n, t) ≡ 1 if E (W) ≤ L 0 otherwise Total time before entry into port = T (n, P) + Q(t) + S(t) Wait at gate, W(t) = Q(t) + S(t) I ≡ Discretization interval Average waiting at xth interval, Historyx = { }
Proposed Agent-based Model (Contd.) Parameters related to ‘Predictors’ S = [s1, s2 ,s3 ,..., sz] ≡ Predictor space containing z predictors k ≡ Number of predictors chosen from S my-predictors-list(n) ≡ Predictor set of depot agent n my-predictors-scores(n) ≡ Rank of predictors of depot agent n my-predictors-estimates(n) ≡ for each predictor sactive−predictor(n) ≡ Best performing predictor for depot agent n Updating of scores Original 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
Model Implementation • Simulation model, coded in Netlogo
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)
Results (Base Case Comparison) • 43% and 63% lower mean wait time for I = 5 and 10 mins • 22% and 40% lower maximum wait time for I = 5 and 10 mins • 18% and 40% higher completion time for I = 5 and 10 mins
Concluding Remarks • Proposed 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