Hybrid genetic algorithms in solving vehicle routing problems with time window constraints

Abstract

This paper describes the authors' research on Genetic Algorithms (GAs) in solving Vehicle Routing Problems with Time Window Constraints (VRPTW). In the vehicle routing problem, a set of vehicles with limited capacity, are to be routed from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. To solve the problem, the optimized assignment of vehicles to each customer is needed as to achieve the minimal total cost without violating the capacity and time window constraints. VRPTW is NP-hard problem and best solved to optimum or near optimum by heuristics. Here we explore the hybrid Genetic Algorithms (hGAs) which combine with local search method to solve the representation problem in the simple GAs. The implemented heuristic is applied to solve Solomon's 56 VRPTW 100-customer instances, and yield 18 solutions better than or equivalent to the best solutions ever published in literature.