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A making-decision system for an urban transportation network
Keywords:
anytime algorithm, network transportation, decision-making, disturbed urban transportation networkAbstract
This paper deals with the real time regulation of traffic within a disturbed transportation system. We show the necessity of a decision support system that detects, analyzes and resolves the unpredicted disturbances. Due to the disturbed aspect of transportation system, we present a multi-agent approach for the regulation process. This approach includes an anytime algorithm, which permits to access to solutions in real time. The quality of the results increases with allocated time. Our system is able to foresee all behaviors according to the environment with which it interacts. These aims offer real guarantees with respect to the temporal deadlines. The main objective is not to search an optimal solution for a disturbance, but to define a set of possible solutions.
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References
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[2] K. Bouamrane, N. Benyettou and B. Beldjilali, Urban Transport Bimodal Network Management using a MultiAgents System Approach. Int Conference on Telecomputing and Information Technology/ ICTIT-IEEE 2004, 22-24 September, Amman-Jordan
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[2] K. Bouamrane, N. Benyettou and B. Beldjilali, Urban Transport Bimodal Network Management using a MultiAgents System Approach. Int Conference on Telecomputing and Information Technology/ ICTIT-IEEE 2004, 22-24 September, Amman-Jordan
[3] B. Fayech Chaar, Régulation des réseaux de transport multimodal: Systèmes Multi-agent et algorithmes évolutionnistes, University of Lille, Phd Thesis, 2003.
[4] T.Dean ., M. Boddy, Solving time-dependant planning problems. Proceedings of the 7th National Conference of Artificial Intelligence, Minneapolis, Minnesota, 1988, p. 419-454.
[5] A. Delhay, M. Dauchet, P.Taillibert, P. Vanheeghe. Optimization of the Average Quality of Anytime Algorithms. ECAI-98 Workshop on Monitoring and Control of Real-Time Intelligent Systems, Brighton, 245 august 1998.
[6] C. Duvallet, B. Sadeg. Des systèmes multiagents anytime pour la conception de systèmes d’aide à la décision. RSTI-TSI. Volume 23–N°8/2004, pp 997-1025.
[7] D. Huisman, R. Freling and A. PM Wagelmans. A Dynamic approach to Vehicle Scheduling. Econometric Institute, Erasmus University Rotterdam, Netherlands Report E12001-17.
[8] E. Horvitz E. Reasonning about beliefs and actions under computational resource constraints. Proceedings of the Workshop on Uncertainty in Artificial Intelligence, Seattle, Washington, 1987.
[9] H. Laichour, Modélisation Multi-agent et aide à la décision: Application à la régulation des correspondances dans les réseaux de transport urbain. University of Lille, Phd Thesis, december 2002.
[10] M. Tendjaoui. Supervision des réseaux multimodaux. Activity Report 2000.
[11] S.R. Thangiah. Vehicle Routing with time Windows using Genetic Algorithms. Artificial Intelligence and robotics Laboratory, Slippery Rock, University USA, applications Handbook of Genetic Algorithms: New Frontiers, Volume II, lance Chambers (Ed.) CRC Press, 1995 pp 253-277.
[12] K.Q. Zhu. A new Genetic Algorithm for VRPTW. Journal of combinatorial Optimization, April 2000, available: http:\\citeseer.nj.ncc.com/311264.html.
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Published
2005-10-03
How to Cite
Bouamrane, K., Tahón, C., & Beldjilali, B. (2005). A making-decision system for an urban transportation network. Journal of Computer Science and Technology, 5(03), p. 144–149. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/863
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