Scheduling of trucks in cross-docking systems: a hybrid meta-heuristic algorithm

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. In this paper, we propose a novel hybrid meta-heuristic algorithm for solving scheduling trucks in cross-docking problems. This algorithm comprises three components: an initial population generation method based on ant colony optimization (ACO), simulated annealing (SA) as an evolutionary algorithm employs a certain probability to avoid becoming trapped in local optimum, and variable neighborhood search (VNS) that involves three local search procedures to improve the population. Moreover, to demonstrate the effectiveness of the proposed methods especially for large-sized problems, various test problems are solved. The computational results demonstrate that our proposed algorithm performs far better than those of Yu and Egbelu (2008).