چكيده به لاتين
The supply chain for perishable goods has always been one of the most important and challenging issues of management discussions at various times. Today, due to the increase in the provision of various types of perishable goods, the variety of perishable goods has increased the complexity of supply chain control. As a result, due to the complexity of inventory management and the planning of inventory capacity and production scheduling, supply chain performance faces recession. In addition, since perishable goods have a direct impact on human health, it is particularly important. Therefore, efficient supply chain management for short-lived goods, especially food, pharmaceuticals, dairy products and ... has the highest importance, and the optimal design of the supply chain network for perishable goods is one of the issues that has always been paid attention by the organizations and should be given special considerations. Hence, in this research, an optimal reverse supply chain model for perishable goods using a variety of vehicles suitable for perishable goods with the aim of maximizing profits has been considered. In this model, the demand is considered indeterminate. Hence, a robust optimal model is proposed to eliminate uncertainty and provide better decision making. Also, it is also solved through meta-heuristic algorithms including Genetics, Particle Swarm and Simulated Annealing algorithms and then compared solving time and objective function of these three algorithms. Based on numerical examples, we conclude that the time to solve by the Particle Swarm algorithm is lower than other algorithms, as well as the value of objective function is in a more favorable manner.
Keywords: Supply Chain, Perishable Goods, Robust Optimization, Meta-heuristic Methods
