A Strength Pareto Evolutionary Algorithm for Solving the Capacitated Vehicle Routing Problem with Time Windows.

The capacitated vehicle routing problem with time windows (CVRPTW) belongs to a set of complex combinatorial optimization problems that find major application in logistic systems and telecommunications. It is a complex variant of the well studied capacitated vehicle routing problem (CVRP). Few meta-heuristic approaches have been proposed to solve the CVRPTW problem in literature. In this paper, we examine a population based meta-heuristic algorithm, more precisely the strength Pareto evolutionary algorithm SPEA2 with hill climbing as a local search. The novelty of the approach lies in the choice of multi objective evolutionary algorithm namely SPEA2, its suggested evolutionary operators, and the optimization for three objectives: the number of routes, the total distance travelled, and the average time per route. The proposed algorithm is implemented using Python, and tested on the Solomon's instances benchmark. Favorable results are reported and suggestions for further improvements are discussed.