ABSTRACT
The vehicle routing problem with time windows (VRPTW) is an extension of the vehicle routing problem with earliest, latest, and service times for customers. The objective of the problem is to minimize the number of vehicles and the distance travelled to service the customers. Heuristic approaches for the VRPTW use route construction, route improvement or methods that integrate both route construction and route improvement. A cluster-first route-second method using Genetic Algorithms and a local post-optimization process was introduced by Thangiah to solve the VRPTW. The chapter aims to develop a hybrid search strategy that combines Genetic Algorithms, Simulated Annealing and Tabu Search methods for solving the VRPTW. The Genetic Sectoring heuristic used for the VRPTW was adapted from the sectoring method used for clustering customers for the VRPTD. Simulated Annealing is a stochastic relaxation technique which has its origin in statistical mechanics. The Simulated Annealing methodology draws its analogy from the annealing process of solids.
