Evolving Dispatching Rules for Dynamic Vehicle Routing with Genetic Programming

Many real-world applications of the vehicle routing problem (VRP) are arising today, which range from physical resource planning to virtual management in cloud computing domain. A common trait these is usually large scale size instances, require fast algorithms generate solutions acceptable quality. The basis for many VRP approaches a heuristic builds candidate solution that may subsequently be improved by local search procedure. Since there variants basic model, specialised must devised take into account specific constraints and user-defined objective measures. Another factor scheduling process carried out dynamic conditions, where future information uncertain or unavailable subject change. When all this considered, need customised heuristics, variant, could used highly environments. In paper, we use genetic programming (GP) evolve suitable dispatching rule build different objectives classes problems, applicable both stochastic conditions. results show great potential, since method performance objectives.