Agent-based container terminal optimisation

Container terminals play a critical role in international shipping and are under pressure to cope with increasing container traffic. The problem of managing container terminals effectively has a number of characteristics which make agents a suitable technology to consider applying. Container terminals involve the operation of distributed entities (e.g. quay cranes, straddle carriers) which coordinate to achieve competing goals in a dynamic environment. This paper describes a joint industry-university project which has explored the applicability of agent technology to the domain of container terminal management. We describe an emulation platform of a container terminal based on the JADE agent framework, along with two optimisations that have been developed and integrated with the emulator: allocating container moves to machines through negotiation, and allocating containers to yard locations through an evolutionary algorithm.Unpublished[1] M. Bazzazi, N. Safaei, and N. Javadian. A genetic algorithm to solve the storage space allocation problem in a container terminal. Computers and Industrial Engineering, 56(1):44–52, 2009. [2] V. J. Botti. Multi-agent system technology in a port container terminal automation. ERCIM News, 56:37–39, January 2004. [3] L. Chen, N. Bostel, P. Dejax, J. Cai, and L. Xi. A tabu search algorithm for the integrated scheduling problem of container handling systems in a maritime terminal. European Journal of Operational Research, 181:40–58, 2007. [4] L. Henesey, P. Davidsson, and J. A. Persson. Agent based simulation architecture for evaluating operational policies in transshipping containers. Autonomous Agents and Multi-Agent Systems, 18:220–238, 2009. [5] M. Kefi, O. Korbaa, K. Ghedira, and P. Yim. Container handling using multi-agent architecture. In N.T. Nguyen et al., editor, KES-AMSTA, volume LNAI 4496, pages 685–693. Springer, 2007. [6] K. H. Kim and Y.-M. Park. A crane scheduling method for port container terminals. European Journal of Operational Research, 156:752–768, 2004. [7] E. Kozan and P. Preston. Mathematical modelling of container transfers and storage locations at seaport terminals. OR Spectrum, 28(4):519–537, October 2006. [8] P. Preston and E. Kozan. An approach to determine storage locations of containers at seaport terminals. Computers & Operations Research, 28(10):983–995, September 2001. [9] M. Rebollo, V. Julian, C. Carrascosa, and V. Botti. A multi-agent system for the automation of a port container terminal. In Workshop on Agents in Industry, 2000. [10] R. Stahlbock and S. Voß. Operations research at container terminals: a literature update. OR Spectrum, 30:1–52, 2008. [11] D. Steenken, S. Voß, and R. Stahlbock. Container terminal operation and operations research — a classification and literature review. OR Spectrum, 26:3–49, 2004. [12] T. Thurston and H. Hu. Distributed agent architecture for port automation. In Proceedings of the 26th Annual International Computer Software and Applications Conference (COMPSAC). IEEE Computer Society, 2002. [13] C. Zhang, J. Liu, Y. Wan, K. Murty, and R. Linn. Storage space allocation in container terminals. Transportation Research Part B-Methodological, 37(10):883–903, December 2003. [14] P. Zhou, H. Kang, and L. Lin. A fuzzy model for scheduling equipments handling outbound container in terminal. In 6th World Congress on Intelligent Control and Automation, pages 7267–7271, June 2006