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

Flow of Containers and Decision Problems Yard Operations - Storage Space Assignment Berth Allocation Quay Crane Scheduling Yard Operations – Yard Crane Scheduling Transport of Containers to Storage Area and Vice Versa Delivery and Receipt Operations (Gate Operations)

Research Topics Two Research Studies Yard Crane Scheduling Problem Truck Queuing at Terminal Gates 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

YCS Problem Solution Solution to YCS Problem Centralized Approaches -OR Optimization - IP - MIP Decentralized Approaches - Agent-based Modeling

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 First Phase (Find Best Partition) How 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

Centralized Approach Second Phase (Job Reassignment) How 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

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 Distance Based Utility Time Based Utility 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

Key Differences Centralized 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.

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 Centralized 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

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 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

Comparative Performance between the two approaches Adaptability 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

Concluding Remarks/ Future Work Two approaches have complimentary solution properties Hybrid approaches may offer better results Proposed Hybrid Approach I Local optimization models for cranes Coordination for best partition within yard zone Proposed Hybrid Approach II Solve global optimization periodically Switch to adaptive agent-based model when necessary

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

Solution to the Gate Congestion Problem Attempted Solution Appointment Systems/ Reservation Systems with Time Windows Real Time Gate Congestion Information Using Webcams

Proposed Agent-based Model

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

Experimental Design Parameter Value Unit Number of Depots 10 Nos Dispatch rate (θ) 12 trucks/depot/hr Mean transaction time (μ) 3, 4, 5, 6, 7 and 8 minutes Tolerance (L) 15, 20, 25 and 30 Total predictors 200 Predictors per depot (k) Nos / depot Update interval (I) 5,10 and 15 Maximum memory (m) 20 intervals Predictor scoring policy Original precision n/a Alpha 0.5

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

Thank You Questions ?