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Exploiting Knowledge About Future Demands for Real-Time Vehicle Dispatching

Département d’opérations et systèmes de décision, and Centre de recherche sur les technologies de l’organisation réseau, Université Laval, Québec, Québec, Canada G1K 7P4
Département d’informatique et de recherche opérationnelle, and Centre de recherche sur les transports, Université de Montréal, C.P. 6128, succursale Centre-ville, Montréal, Québec, Canada H3C 3J7
Département d’informatique et de recherche opérationnelle, and Centre de recherche sur les transports, Université de Montréal, C.P. 6128, succursale Centre-ville, Montréal, Québec, Canada H3C 3J7
soumia.ichoua{at}fsa.ulaval.ca
michelg{at}crt.umontreal.ca
potvin{at}iro.umontreal.ca

An important, but seldom investigated, issue in the field of dynamic vehicle routing and dispatching is how to exploit information about future events to improve decision making. In this paper, we address this issue in a real-time setting with a strategy based on probabilistic knowledge about future request arrivals to better manage the fleet of vehicles. More precisely, the new strategy introduces dummy customers (representing forecasted requests) in vehicle routes to provide a good coverage of the territory. This strategy is assessed through computational experiments performed in a simulated environment.

Key Words: vehicle dispatching; real time; probabilistic knowledge; future events; parallel tabu search
History: Received: August 2001; revised: October 2004; accepted: January 2005.

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