Restricted dynamic programming: A flexible framework for solving realistic VRPs

Abstract

Most successful solution methods for solving large vehicle routing and scheduling problems are based on local search. These approaches are designed and optimized for specific types of vehicle routing problems (VRPs). VRPs appearing in practice typically accommodate restrictions that are not accommodated in classical VRP models, such as time-dependent travel times and driving hours regulations. We present a new construction framework for solving VRPs that can handle a wide range of different types of VRPs. In addition, this framework accommodates various restrictions that are not considered in classical vehicle routing models, but that regularly appear in practice. Within this framework, restricted dynamic programming is applied to the VRP through the giant-tour representation. This algorithm is a construction heuristic which for many types of restrictions and objective functions leads to an optimal algorithm when applied in an unrestricted way. We demonstrate the flexibility of the framework for various restrictions appearing in practice. The computational experiments demonstrate that the framework competes with state of the art local search methods when more realistic constraints are considered than in classical VRPs. Therefore, this new framework for solving VRPs is a promising approach for practical applications.