Thesis Defense Investigation of Agent-Based Approaches to Enhance Container Terminal Operations by

1. 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

2. 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

3. 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)

4. 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.

5. 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

6. 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

7. 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

8. Yard Crane Scheduling (YCS) Problem Motivation • Operational improvement of container terminal • Reducing drayage trucks turn time • Efficient allocation of scarce resources • Environmental Concerns

9. YCS Problem Solution

10. Research Questions • Comparative Study between the two approaches • Contrasting assumptions? • Strengths and weaknesses? • Relative performances? • Suitability for implementation?

11. 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

12. Centralized Approach How the Heuristic solves YCS? Heuristic has TWO phases

13. Centralized Approach How the Heuristic solves YCS? Heuristic has TWO phases

14. Centralized Approach A Sample Heuristic Solution First Phase Solution Second Phase Solution Path of the Cranes

15. 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

16. 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

17. Decentralized Approach • Simulation model, coded in Netlogo • Netlogo: A multi-agent programmable Environment

18. Key Differences

19. 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

20. Comparative Performance between the two approaches Optimality - Minimize the truck waiting time

21. Comparative Performance between the two approaches Optimality - Minimize the truck waiting time Fig: Mean Index for different truck arrival rates

22. 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

23. Comparative Performance between the two approaches Scalability and computational efficiency

24. Comparative Performance between the two approaches Adaptability

25. Concluding Remarks/ Future Work • Two approaches have complimentary solution properties • Hybrid approaches may offer better results

26. 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

27. Outline • Gate Congestion problem at CT • Proposed Model and Implementation • Design of Experiments and Results • Concluding Remarks

28. 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

29. Solution to the Gate Congestion Problem

30. Proposed Agent-based Model

31. 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 = { }

32. 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

33. Proposed Agent-based Model (Contd.) Pseudo Code of the Program – Part of the Main Loop

34. Model Implementation • Simulation model, coded in Netlogo

35. Experimental Design

36. Results (Mean wait and Total completion) Fig: Impact of tolerance on mean wait time of trucks Fig: Impact of tolerance on total completion time.

37. 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)

38. 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

39. 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

40. Thank YouQuestions ?