The container drayage problem for heterogeneous trucks with multiple loads: A revisit. (March 2021) - Chen, Rui; Chen, Shukai; Cui, Haipeng; Meng, Qiang
Highlights: Revisit the container drayage problem (CDP) for heterogeneous trucks with multiple loads. Investigate a new variant of CDP with practical features and propose tailored formulation. Introduce two relaxed models with significantly less decision variables. Develop a hybrid heuristic to deal with the problem on large scale. Draw insights from comparison and sensitivity analysis. Abstract: In this study, we revisit the container drayage problem for heterogeneous trucks with multiple loads in view of its practical significance. We first propose a tailored mixed-integer linear programming (MILP) model explicitly to reflect the reality of container drayage operations. To solve medium-scale problems more effectively, two relaxed MILP models are built to provide a solution that is close to optimal with less computational time. These two relaxed models are proven to work as intended and provide optimal solutions under certain conditions. Moreover, an effective hybrid heuristic method incorporating the cheapest feasible insertion mechanism and the variable neighborhood search scheme is designed to solve large-scale problems. Extensive numerical experiments demonstrate the scalability of the heuristic method in real-world applications.