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

Automated summary

Many real-world applications of the vehicle routing problem (VRP) require fast algorithms to generate solutions of acceptable quality for large scale problem instances. The basis for many VRP approaches is a heuristic that builds a candidate solution, which can be improved by a local search procedure. Customised heuristics are needed for specific problem variants in highly dynamic environments, where future information may be uncertain or subject to change.