Proposing a Multi-objective Imperialist Competitive Algorithm to Optimize Reliable Aggregate Production Planning Problem

In this paper, a bi-objective model is developed to deal with an aggregate production planning problem in a multi product, multi period supply chain including multiple suppliers, factories and demand points. This bi-objective model aims to minimize the total cost of supply chain including inventory costs, manufacturing costs, work force costs, hiring, and firing costs, and maximize the minimum of producer's reliability by considering probabilistic lead times, to improve performance of the system and achieve a more reliable production plan. Since the proposed bi-objective model is NP-hard, a Pareto-based multi-objective imperialist competitive algorithm (MOICA) is used. To evaluate the performance of presented algorithm, non-dominated sorting genetic algorithm (NSGA-II) is applied, too. The results show the capability and efficiency of proposed algorithm in finding Pareto solutions.