Designing a reliable aggregate production planning problem during the disaster period

The purpose of this research is to introduce a Bi-Objective (BO) model for dealing with Aggregate Production Planning (APP) for a multi-product and multi-period Supply Chain Network (SCN) that incorporates multiple suppliers, factories, and demand points. One of the goals of the model is to minimize the total cost of this network during the disaster period. The other goal is to account for probabilistic lead times to maximize the minimum level of producers' reliability during the COVID-19 pandemic. They are done to ameliorate the system's performance and improve the reliability of production plans. Finally, considering that the mentioned problem is NP-hard, a Multi-Objective Imperialist Competitive Algorithm (MOICA) based on Pareto is used to solve the proposed model, and a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is also utilized to measure the performance of the mentioned algorithm. The generated experimental problems' results demonstrate the proposed algorithm's power in finding Pareto solutions. According to innovation, this is the first paper on these topics considering the conditions of the COVID-19 disaster.