Heuristics for an oil delivery vehicle routing problem

Abstract

Companies distributing heating oil typically solve vehicle routing problems on a daily basis. Their problems may involve various features such as a heterogeneous vehicle fleet, multiple depots, intra-route replenishments, time windows, driver shifts and optional customers. In this paper, we consider such a rich vehicle routing problem that arises in practice and develop three metaheuristics to address it, namely, a tabu search (TS) algorithm, a large neighborhood search (LNS) heuristic based on this TS heuristic and another LNS heuristic based on a column generation (CG) heuristic. Computational results obtained on instances derived from a real-world dataset indicate that the LNS methods outperform the TS heuristic. Furthermore, the LNS method based on CG tends to produce better quality results than the TS-based LNS heuristic, especially when sufficient computational time is available.