This work presents a multi objective model for a multi-product, multi-site aggregate production planning (APP) problem in a supply chain. The objectives are: 1) minimising the total cost of supply chain including inventory costs, manufacturing costs, work force costs, hiring, and firing costs; 2) maximising the reliability of the supply chain’s production plan with regard to the selection of reliable facilities which have probabilistic lead times. Based on the complexity of the proposed model, two Pareto-based multi-objective metaheuristic algorithms including multi-objective imperialist competitive algorithm (MOICA) and non-dominated sorting genetic algorithm (NSGA-II) were applied to solve the proposed model. In order to evaluate the results, several numerical examples were generated, by which the algorithms were analysed statistically and graphically.
