Presentation at TRB 90th Annual Meeting

1. 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 Presented at the Joint Meeting of the Ports and Channels Committee (AW010) and the Intermodal Freight Terminal Design and Operations Committee (AT050)

2. 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

3. 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

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

5. YCS Problem Solution

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

7. 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

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

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

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

11. 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

12. 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

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

14. Key Differences

15. 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 • Number of Jobs ⟶ 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

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

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

18. 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

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

20. Comparative Performance between the two approaches Adaptability

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

22. Thank YouQuestions ?