Evolving Dispatching Rules for Dynamic Vehicle Routing with Genetic Programming

Abstract

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.

Review of Stochastic Dynamic Vehicle Routing in the Evolving Urban Logistics Environment

“…In another study, the authors provided a definition of algorithm building blocks and a measure of algorithm quality, where generative hyper-heuristics employ automated processes to evolve heuristics tailored to specific problem instances [43]. Unlike selective hyper-heuristics, which dynamically choose from pre-existing heuristics, generative heuristics explore algorithmic spaces, crafting novel heuristics optimised for unique routing challenges, demonstrating adaptability in dynamic and stochastic conditions [43]. Due to its flexible representation, one of the most prominent methods for automated heuristic generation methods is Genetic Programming (GP) [44].…”

“…Due to its flexible representation, one of the most prominent methods for automated heuristic generation methods is Genetic Programming (GP) [44]. One may find examples of approaches utilising GP in the domain of DVRPs and Electric Vehicle-DVRPs in the works presented in [43][44][45][46].…”

A Variable Neighborhood Search Method with a Tabu List and Local Search for Optimizing Routing in Trucks in Maritime Ports

“…However, since these objectives are often not sufficient, more advanced methods incorporate additional constraints. Some of these include balancing the workload among vehicles [2], coordinating adjacent shifts [3], avoiding traffic congestion [4], and dynamically updating vehicle routes [5]. The optimization objectives depend on the specific problem and customer requirements, and there are numerous different objectives that often need to be balanced [6].…”