A hybrid approach for scheduling transportation networks

• Autorzy: Dridi M. , Kacem I.
• Języki publikacji: EN

Abstrakty

EN

In this paper, we consider a regulation problem of an urban transportation network. From a given timetable, we aim to find a new schedule of multiple vehicles after the detection of a disturbance at a given time. The main objective is to find a solution maximizing the level of service for all passengers. This problem was intensively studied with evolutionary approaches and multi-agent techniques, but without identifying its type before. In this paper, we formulate the problem as a classical one in the case of an unlimited vehicle capacity. In the case of a limited capacity and an integrity constraint, the problem becomes difficult to solve. Then, a new coding and well-adapted operators are proposed for such a problem and integrated in a new evolutionary approach.

Słowa kluczowe

EN

transportation systems; traffic regulation; genetic algorithm; multicriteria optimization

PL

system transportowy; regulacja ruchu; algorytm genetyczny; optymalizacja wielokryterialna

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2004
• Tom: Vol. 14, no 3
• Strony: 397--409
• Opis fizyczny: Bibliogr. 11 poz., rys., tab., wykr.

Twórcy

autor

Dridi M.

• LAGIS, Ecole Centrale de Lille, France

autor

Kacem I.

• ISTIT, Université de Technologie de Troyes, France

Bibliografia

• [1] Aloulou M. (1999): Application des algorithmes génétiques à la régulation du trafic des bus. — Mémoire de DEA en Informatique Industrielle, Université des Sciences et Technologies de Lille, France.
• [2] Banzhaf W., Nordin P., Keller R.E. and Francone F.D. (1998): Genetic Programming. An Introduction on the Automatic Evolution of Computer Programs and Its Application. — San Francisco: Morgan Kaufmann.
• [3] Burke E.K. and Smith A.J. (2000): Hybrid evolutionary techniques for the maintenance scheduling problem. — IEEE Trans. Power Syst. Vol. 15, No. 1, pp. 122–128.
• [4] Dasgupta D. and Michalewicz Z. (1997): Evolutionary Algorithms in Engineering Applications. — Berlin: Springer.
• [5] Fayech B. (2000): Application des Algorithmes Evolutionnistes aux problèmes de Régulation du Trafic des Autobus. — Rapport de DEA, Université de Lille 1, UFR IEEA, France.
• [6] Fayech B., Hammadi S., Maouche S. and Borne P. (2001): Urbain bus traffic regulation by evolutionary algorithms. — Proc. IEEE/SMC’01 Conf., Tucson, USA, pp. 1316–1322.
• [7] Fayech B., Hammadi S., Maouche S. and Borne P. (2002a): Approche coopérative pour la régulation d’un réseau de transport urbain multimodal. — Proc. Conférence Internationale Francophone d’Automatique, CIFA’02, Nantes, France, pp. 177–182.
• [8] Fayech B., Hammadi S., Maouche S. and Borne P. (2002b): An evolutionary rescheduling approach for an urban transport network with simultaneous disturbances.—Proc. 2nd Conf. JETA 2002, Sousse, Tunisia.
• [9] Fonseca, C.M. and Fleming P.J. (1998): Multiobjective optimization and multiple constraint handling with evolutionary algorithms-Part I: Unified formulation. — IEEE Trans/SMC, Part A, Vol. 28, No. 1, pp. 26–37.
• [10] Goldberg D.E. (1989): Genetic Algorithms in Search, Optimization, and Machine Learning. — Reading, MA: Addison Wesley.
• [11] Kacem I., Hammadi S. and Borne P. (2003): Fuzzy Evolutionary Approach for Multi-objective Combinatorial Optimization: Application to Scheduling Problems. Fuzzy Sets-Based Heuristics for Optimization.—Springer.