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Cross-Docking (CD) is a modern distribution strategy in a supply chain. The optimal scheduling of vehicle routing, known as the Vehicle Routing Problem (VRP), is one of the influential factors of the efficiency of a supply chain. In recent years, researchers and business consultants in different organizations have been interested in integrating the VRP with CD (VRPCD). Since VRPCD is a NP-hard problem, heuristic or meta-heuristic methods are always recommended to solve large-scale VRPCD. The Genetic Algorithm (GA) is a population based meta-heuristic algorithm and also, it is based on the principles of genetic and natural selections. The GA is capable of finding near optimal solutions to large-scale optimization problems which are extremely difficult to solve using traditional optimization algorithms. Therefore, in this study, a meta-heuristic approach based on the GA is proposed to solve the vehicle routing problem with moving shipments at the cross-docking facility (VRPCD&MS). The data are extracted from benchmark instances in the literature. The optimum solutions obtained to small-scale instances by the GA are compared with the exact solutions obtained by the Branch and Bound (BB) algorithm, which is a traditional algorithm to solve problems of this nature. The GA and BB algorithms are respectively coded in MATLAB and LINGO. The results reveal that the relative difference between the exact solution and the near–optimal solution is below 5%. Therefore, it can be concluded that the proposed GA is a better alternative method, considering its overall performance, to solve the VRPCD&MS models. Moreover, since the computational time is low, the proposed GA can be used to schedule the vehicles to the routes of VRPCD&MS at the last moment prior to the start of the time horizon.
Gnanapragasam et al. (Tue,) studied this question.