Evolutionary algorithms and fuzzy clustering for control of a dynamic vehicle routing problem oriented to user policy

Abstract

In this paper, a dynamic vehicle routing problem (DVRP) is solved based on hybrid predictive control strategy with an objective function that includes two dimensions: user and operator costs. To handle some undesired assignments for the users, a new objective function is designed, able to carry out the fact that some users can become particularly annoyed if their service is postponed. Genetic algorithms are proposed for efficiently solving the DVRP. Fuzzy clustering is applied for computing trip patterns from historical data under more realistic scenarios. An illustrative experiment through simulation of the process is presented to show the potential benefits (mainly for users) of the new design.