A NOVEL APPROACH FOR SOLVING THE DYNAMIC VEHICLE ROUTING PROBLEM BASED ON HIERARCHICAL SELF-ORGANIZING MAPS

Abstract

Dynamic routing problems ask for policies and algorithms to build reactive plans able to integrate dynamically generated requests in currently running plans. The main desired quality of such approaches is to overcome the lack of information availability in the beginning of the solving process. That is by proposing flexible partial solutions anticipating future requests. In this paper, we propose a neural model based on Hierarchical self-organizing maps (HSOM) for solving the dynamic vehicle routing problem (DVRP). The DVRP consists of finding routes for a set of vehicles to serve customer's dynamically issued requests while minimizing routing costs and satisfying some constraints. We present the concept of problem control bloc (PCB) to represent the problem to solve by the HSOM algorithm when triggered by a new event. The overall solving approach is integrated within a discrete event manager that will wait for any new event to build the PCB, call the HSOM solver, get the proposed solution and update the running plan accordingly. The proposed approach was applied to solve the DVRP problems adapted from VRP benchmarks. The obtained results are compared to the best known solutions.