A Genetic Algorithm for Integration of Vehicle Routing Problem and Production Scheduling in Supply Chain (Case Study: Medical Equipment Supply Chain)

This paper studies a model for integration of vehicle routing problem (VRP) in a supply chain with order assignment to the suppliers and determining their production sequence. The considered supply chain consists of some suppliers, vehicles and a manufacturer. It is assumed that manufacturer purchases identify the raw material demand of suppliers in wholesale all at once. This provides the opportunity of receiving discounts and consequently decreasing final price. A transportation fleet composed of some vehicles, each of which may have a different speed and different transport capacity, is responsible for transporting purchased raw materials to suppliers and gathering completed parts from them aiming at minimizing the total tardiness of all jobs. After presenting the mathematical model of the problem, a dynamic genetic algorithm with two dimensional structures is proposed. The algorithm was applied to the supply chain of a medical equipment manufacturer and the results were compared with real results beforehand. Findings show that applying dynamic genetic algorithm results in improving the average of tardiness from 9.44 days to 2.11 days. Also the comparison of dynamic genetic algorithm with the optimum solution for the small size problems, and the algorithm proposed for the nearest problem in the literature to our problem shows the high efficiency of dynamic genetic algorithm.