Optimising load planning and container routing in intermodal rail transport

Abstract

With the rising focus towards more sustainable transport systems, intermodal transport is a promising alternative for unimodal road transport. However, it also presents a number of challenges for intermodal operators and planners who are in charge of executing transport planning in an increasingly dynamic environment. As multiple transport modes and decision makers are involved, intermodal transport is more complex. Moreover, the level of integration of different transport modes and decision levels influences the attractiveness of intermodal transport. Innovative and integrated transport systems reflecting real-life problems should be developed in order to provide adequate decision support tools for intermodal planners.

This dissertation aims at offering intermodal planning support in order to minimise total transport costs and maximise service capacity utilisation, which in turn decreases costs of the transport system. Two decision support tools are proposed by means of fast planning algorithms which include real-life characteristics. These concepts are inherent to the synchromodal vision in order to encourage a modal shift away from unimodal road transport. After a literature review on intermodal routing and vehicle routing in intermodal transport, two problems usually considered separately, an integrated intermodal routing problem is presented. By including information of local vehicle routes in the assignment of transport requests to long-haul routes through a service network, better-informed decisions can be obtained. Insights are presented on how such integrated approach can be used in practice by means of a real-life case. With transport requests assigned to intermodal routes through an intermodal rail service network, they should be assigned to specific locations on an intermodal train, which is the train load planning problem. It includes detailed loading restrictions, as omitting relevant loading constraints could result in infeasible solutions. Both an exact and a heuristic solution approach with multiple objectives and additional real-life loading constraints are presented, resulting in multiple feasible train load plans. Routes might still change for some transport requests and information about future orders should be accounted for. By providing multiple plans, planners can select the most appropriate load plan at a specific moment in time. The problem is applied to a real-life case to demonstrate advantages for practitioners.