Supply Chain Scheduling Using a Transportation System Composed of Vehicle Routing Problem and Cross-Docking Approaches

Abstract

This study considers a combination of cross-docking and vehicle routing problem (VRP) approaches<br />to transport raw material and parts in a supply chain. The supply chain is composed of some suppliers<br />which are spread in different geographical zones and multiple shared vehicles with different speeds<br />and capacities for transporting orders from the suppliers to a manufacturer. After proposing a<br />mathematical model of this new problem, a developed version of genetic algorithm based on a<br />psychological theory, named Reference Group Genetic Algorithm (RGGA) is used to solve the<br />problem. The originality of this research is proposing a new method in integrated production and<br />transportation scheduling in supply chain by combination of cross-docking and VRP approaches,<br />presenting the mathematical model of the problem and adapting RGGA to solve it. To evaluate<br />RGGA performance, we develop a genetic algorithm proposed for the nearest problem in literature<br />and compare these two algorithms. Moreover, RGGA results are compared with optimum solutions<br />by some low size test problems. The result shows the good performance of RGGA.