Information Technology Reference
In-Depth Information
our optimisations are effective, e.g. that the EA model is able to improve upon random
yard allocations.
Overall, our conclusion is that taking an agent-based approach has proven to be a nat-
ural choice because of the nature of ports in which many actors work together with a high
degree of autonomy. The agent paradigm supports the modeling of such an environment
with a high level of detail, flexibility and consistency with the archetype by assuring
as little transfer friction as possible. The use of a free and mature agent framework re-
duced development overhead and enabled access to easy exploitation of parallelization
potential and advanced features like agent mobility for further development.
There is a range of directions for future work. One direction is to allow the replace-
ment of the centralized module agents by a distributed solution. The challenge is, among
others, that the optimisation phase currently considers a very large number of possible
reallocations, and we need to reduce this for a distributed implementation to be practi-
cal. We are in the process of investigating heuristics for doing this reduction. Finally,
regarding the EA-based yard allocation mechanism, our hypothesis that an order-based
EA is required to optimise the complete yard allocation problem is as yet untested.
We plan to conduct experimental tests against a control random allocation policy, and
against the current 'block allocation' heuristic used by the local port.
References
1. Thurston, T., Hu, H.: Distributed agent architecture for port automation. In: Proceed-
ings of the 26th Annual International Computer Software and Applications Conference
(COMPSAC). IEEE Computer Society Press, Los Alamitos (2002)
2. Rebollo, M., Julian, V., Carrascosa, C., Botti, V.: A multi-agent system for the automation of
a port container terminal. In: Workshop on Agents in Industry (2000)
3. Henesey, L., Davidsson, P., Persson, J.A.: Agent based simulation architecture for evaluat-
ing operational policies in transshipping containers. Autonomous Agents and Multi-Agent
Systems 18, 220-238 (2009)
4. Steenken, D., Voß, S., Stahlbock, R.: Container terminal operation and operations research
— a classification and literature review. OR Spectrum 26, 3-49 (2004)
5. Stahlbock, R., Voß, S.: Operations research at container terminals: a literature update. OR
Spectrum 30, 1-52 (2008)
6. Chen, L., Bostel, N., Dejax, P., Cai, J., Xi, L.: 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)
7. Zhang, C., Liu, J., Wan, Y., Murty, K., Linn, R.: Storage space allocation in container termi-
nals. Transportation Research Part B-Methodological 37(10), 883-903 (2003)
8. Preston, P., Kozan, E.: An approach to determine storage locations of containers at seaport
terminals. Computers & Operations Research 28(10), 983-995 (2001)
9. Glover, F.: Future paths for integer programming and links to artificial intelligence. Comput-
ers & Operations Research 13(5), 533-549 (1986)
10. De Jong, K.A.: Evolutionary computation: a unified approach. MIT Press, Cambridge (2006)
