Yard crane scheduling at container terminals: A comparative study of centralized and decentralized approaches

This article presents a comparative study of two contrasting approaches for modeling the yard crane scheduling problem: centralized and decentralized. It seeks to assess their relative performances and factors that affect their performances. Our analysis shows that the centralized approach outperforms the decentralized approach by 16.5 per cent on average, due to having complete and accurate information about future truck arrivals. While it underperforms the centralized, the decentralized approach can dynamically adapt to real-time truck arrivals, making it better suited for real-life operations. Overall, our analysis suggests that the two approaches offer complementary features that could be integrated into a hybrid approach.

[1]  Giulia Andrighetto,et al.  What Do Agent-Based and Equation-Based Modelling Tell Us About Social Conventions: The Clash Between ABM and EBM in a Congestion Game Framework , 2010, J. Artif. Soc. Soc. Simul..

[2]  Huosheng Hu,et al.  Distributed agent architecture for port automation , 2002, Proceedings 26th Annual International Computer Software and Applications.

[3]  Lazaros G. Papageorgiou,et al.  A combined optimization and agent-based approach to supply chain modelling and performance assessment , 2001 .

[4]  Paul Davidsson,et al.  Agent based simulation architecture for evaluating operational policies in transshipping containers , 2006, Autonomous Agents and Multi-Agent Systems.

[5]  K. L. Mak,et al.  Yard crane scheduling in port container terminals , 2005 .

[6]  Stefan Voß,et al.  Container terminal operation and operations research — a classification and literature review , 2004 .

[7]  Flaminio Squazzoni,et al.  Why Bother with What Others Tell You? An Experimental Data-Driven Agent-Based Model , 2010, J. Artif. Soc. Soc. Simul..

[8]  Weijian Mi,et al.  A hybrid parallel genetic algorithm for yard crane scheduling , 2010 .

[9]  José M. Vidal,et al.  An agent-based approach to modeling yard cranes at seaport container terminals , 2010, SpringSim.

[10]  Deborah L Thurston,et al.  Utility Function Fundamentals , 2006 .

[11]  Paul Davidsson,et al.  Evaluation of Automated Guided Vehicle Systems for Container Terminals Using Multi Agent Based Simulation , 2009, MABS.

[12]  Stefan Voß,et al.  Operations research at container terminals: a literature update , 2007, OR Spectr..

[13]  Paul Davidsson,et al.  On the Integration of Agent-Based and Mathematical Optimization Techniques , 2007, KES-AMSTA.

[14]  Chung-Lun Li,et al.  Interblock Crane Deployment in Container Terminals , 2002, Transp. Sci..

[15]  Qiang Meng,et al.  Scheduling of two-transtainer systems for loading outbound containers in port container terminals with simulated annealing algorithm , 2007 .

[16]  Wei Chen,et al.  Decision Making in Engineering Design , 2006 .

[17]  Carlos Carrascosa,et al.  A MAS Approach for Port Container Terminal Management: The Transtainer Agent , 2001 .

[18]  Richard J. Linn,et al.  Rubber tired gantry crane deployment for container yard operation , 2003, Comput. Ind. Eng..

[19]  Mark Goh,et al.  Discrete time model and algorithms for container yard crane scheduling , 2009, Eur. J. Oper. Res..

[20]  Cathy Macharis,et al.  Opportunities for OR in intermodal freight transport research: A review , 2004, Eur. J. Oper. Res..

[21]  Jaime Simão Sichman,et al.  Multi-Agent-Based Simulation , 2002, Lecture Notes in Computer Science.

[22]  Hark Hwang,et al.  Sequencing delivery and receiving operations for yard cranes in port container terminals , 2003 .

[23]  Richard J. Linn,et al.  Dynamic crane deployment in container storage yards , 2002 .

[24]  Byung-In Kim,et al.  A Hybrid Scheduling and Control System Architecture for Warehouse Management , 2022 .

[25]  W. C. Ng,et al.  Crane scheduling in container yards with inter-crane interference , 2005, Eur. J. Oper. Res..

[26]  Iris F. A. Vis,et al.  Transshipment of containers at a container terminal: An overview , 2003, Eur. J. Oper. Res..

[27]  Rob A. Zuidwijk,et al.  Can agents measure up? A comparative study of an agent-based and on-line optimization approach for a drayage problem with uncertainty , 2010 .

[28]  Robert Harrison,et al.  Characteristics of Drayage Operations at the Port of Houston, Texas , 2007 .

[29]  Luca Maria Gambardella,et al.  Simulation and Planning of an Intermodal Container Terminal , 1998, Simul..

[30]  Paul Davidsson,et al.  Agent-based Appoaches and Classical Optimization Techniques for Dynamic Distributed Resource Allocation : A preliminary study , 2003 .

[31]  Genevieve Giuliano,et al.  Reducing port-related truck emissions: The terminal gate appointment system at the Ports of Los Angeles and Long Beach , 2007 .