Internal resource requirements: The better performance metric for truck scheduling?. (September 2021) - Wolff, Pascal; Emde, Simon; Pfohl, Hans-Christian
Highlights: A truck scheduling model that minimizes the resource requirements is proposed. A powerful column generation-based solution procedure is developed. Operational efficiency can be significantly increased. Level truck schedules with superior resource utilization. Abstract: Truck scheduling, which assigns a dock-door and a processing interval to each inbound and outbound truck, is an essential operational decision problem in cross-docking platforms and distribution centers. It has attracted considerable academic attention. Most studies, however, have neglected internal resources (e.g., workers or material handling equipment) and hence failed to address two major concerns of cross-docking practitioners: (i) determining the number of resources needed, and (ii) scheduling the internal resources in an efficient way. This study sets out to examine the value of utilizing the internal resource requirements as the main performance metrics in truck scheduling. The problem considered in this paper is how to schedule a set of inbound trucks with time windows at a multidoor cross-docking platform, where the departure times of outbound trucks follow a given schedule. The goal is to identify a feasible truck schedule that can be executed with a minimum number of internal resources. For this setting, a mixed-integer programming model is proposed. Furthermore, a column generation-based solution procedure is developed. We show that by using the internal resource requirements as the main performance measure, the operational efficiency of the cross-docking platform can be significantly increased. It also helps to avoid large peak workloads and leads to level truck schedules with superior resource utilization levels. Computational experiments show that the proposed heuristic algorithm can obtain high-quality solutions for very large problem instances within a short computation time. Due to its tight lower bound, the solution procedure can even prove optimality for most problem instances.