Solving Vehicle Routing Problems Using Constraint Programming and Metaheuristics

Constraint Programming typically uses the technique of depth-first branch and bound as the method of solving optimisation problems. Although this method can give the optimal solution, for large problems, the time needed to find the optimal can be prohibitive. This paper introduces a method for using iterative improvement techniques within a Constraint Programming framework, and applies this technique to vehicle routing problems. We introduce a Constraint Programming model for vehicle routing, after which we describe a system for integrating Constraint Programming and iterative improvement techniques. We then describe how the method can be greatly accelerated by handling core constraints using fast local checks, while other more complex constraints are left to the constraint propagation system. We have coupled our iterative improvement technique with a meta-heuristic to avoid the search being trapped in local minima. Two meta-heuristics are investigated: a simple Tabu Search procedure and Guided Local Search. An empirical study over benchmark problems shows the relative merits of these two techniques. Investigations indicate that the specific long-term memory technique used by Guided Local Search can be incorporated within Tabu Search, resulting in some benefit.