No abstract

The pickup and delivery problem with transfers generalizes the classical pickup and delivery problem (PDP) by allowing the vehicles to exchange request loads at designated transfer points. Transfers often lead to substantial reductions in transportation costs, yet they come with a significant burden of additional computational complexity. Even meta-heuristic methods are thus limited to instances of at most lower hundreds of requests leaving the desirable benefits unreachable for larger instances. Our approach bypasses the complexities inherent to current methods by deciding about the transfers apriori and thus reducing the problem to a PDP instance. To make as informed decisions as possible, we analyze a broader set of characteristics that may be used to carry out the apriori decisions. We opt to derive and examine multiple such PDP instances to cover different transfer choices. Our analysis of the derived PDP instances then allows their efficient processing in parallel. The proposed framework addresses a large-scale freight transportation problem with real-world characteristics and transfers where typical instances count over 1,200 requests and 300 vehicles. We show the potential of the proposed framework on both real-world and synthetic instances with up to 1,500 requests. The experiments demonstrate that substantial savings may be achieved within favorable runtimes even for very large instances.

Interest in vehicle routing problems (VRP) with stochastic and dynamic elements has grown in the past decade. Despite numerous contributions in this area, the handling of uncertainties and dynamic changes in complex VRPs received little attention. Based on our experience from industrial practice, we discuss why accounting for uncertainties and dynamic changes is crucial for the applicability of the produced routing plans. Then, we first identify and justify the best-suited direction to address dynamicity and uncertainties in real-world VRPs. Second, we outline the key concepts and ideas of our approach to finally demonstrate that it is realistic to implement them efficiently.