A Pareto approach for the multi-factory supply chain scheduling and distribution problem

Integrated decisions in the supply chain are one of the most attractive topics for researchers. But to get closer to the real-world problems, other real assumptions should be considered. One of these assumptions is the multi-agent view in which several sets of customers or agents with their own objective compete with each other to acquire the supply chain resources. Here, an integrated supply chain scheduling problem along with the batch delivery consideration in a series multi-factory environment is investigated and the routing decisions among customers are considered. A mathematical model is presented for this problem. Due to the complexity, a novel ant colony optimization algorithm is developed to obtain Pareto solutions. Also, a simulated annealing based local search is used to improve the quality of solutions. The performance of the algorithm is compared with three well-known multi-objective algorithms. Results show the proper performance of the proposed algorithm compared to the other algorithms.