Scheduling trucks in cross-docking systems: Robust meta-heuristics

Abstract

Cross-docking is a logistics technique that minimizes the storage and order picking functions of a warehouse while still allowing it to serve its receiving and shipping functions. The idea is to transfer shipments directly from incoming to outgoing trailers without storage in between. In this paper we apply five meta-heuristic algorithms: genetic algorithm (GA), tabu search (TS), simulated annealing (SA), electromagnetism-like algorithm (EMA) and variable neighbourhood search (VNS) to schedule the trucks in cross-dock systems such that minimize total operation time when a temporary storage buffer to hold items temporarily is located at the shipping dock. A design procedure is developed to specify and adjust significant parameters for GA, TS, SA, EMA and VNS. The proposed procedure is based on the response surface methodology (RSM). Two different types of objective functions are considered to develop multiple objective decision making model. For the purpose of comparing meta-heuristics, makespan and CPU time are considered as two response variables representing effectiveness and efficiency of the algorithms. Based on obtained results, VNS is recommended for scheduling trucks in cross-docking systems. Also, since for real size problems, it is not possible to reach optimum solution, a lower bound is presented to evaluate the resultant solutions.