An Improved Large Neighborhood Search Algorithm for the Comprehensive Container Drayage Problem with Diverse Transport Requests

Container drayage, as a pivotal element of door-to-door intermodal transportation, has garnered increasing attention due to its significant influence on container logistics costs. Although various types of transport requests have been defined in the literature, no comprehensive study has addressed all of them together yet, due to the lack of an efficient model and corresponding algorithms. Furthermore, existing research on container drayage often neglects the simultaneous incorporation of two trucking operation modes, two empty container repositioning strategies, and the availability of empty containers across multiple depots. To address these issues, this study proposes a comprehensive container drayage problem (CDP) and mathematically formulates it as an innovative mixed integer linear programming (MILP) model, capturing the uncertainty and unpredictability inherent in empty container allocation, truck dispatching, and route planning. Given the problem’s complexity, obtaining an exact solution for large instances is not feasible. Therefore, an improved large neighborhood search (LNS) algorithm is tailored by incorporating the “Sequential insertion” and the “Solution re-optimization” operations. Extensive numerical experiments using randomly generated instances of varying scales validate the correctness of the proposed model and demonstrate the performance of the proposed algorithm. Additionally, sensitivity analysis on the number and distribution of depots and empty containers offers valuable managerial insights for the development of an effective container drayage system.