Supply Chain Scheduling Using A Transportation System Composed of Vehicle Routing Problem and Crossdocking Approaches

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