Optimisation de problème de tournées de véhicules de service à domicile

The logistics performance of enterprises and the optimization of transportation have become a great issue in recent years. Field force planning and optimization is a new challenge for the service sector. In order to increase productivity and reduce cost of logistics, this research contributes to the optimization of a real-life multi-depot multi-period field service routing problem with time window. The problem is abstracted from the realistic problem and formulated as a Mixed Integer Programming (MIP) model. Computational results with Cplex show that this problem cannot be solved by exact methods in reasonable time for practical use. First, local search heuristics are used for solving the problem. Initial feasible solutions are generated by a constructive heuristic and several local search heuristics are applied to obtain solutions in a very short computing time. Then we propose a genetic algorithm with new representation of chromosome and new genetic operators for the addressed problem. Finally we consider a genetic algorithm with diversity control to deal with large scale problems. Infeasible solutions are taken account in the population and the diversity contribution is part of the evaluation to avoid premature of search. These methods have been successfully implemented to the optimization of the routing problem