Agile two-stage lot-sizing and scheduling problem with reliability, customer satisfaction and behaviour under uncertainty: a hybrid metaheuristic algorithm

This article proposes a new multi-objective model for a lot-sizing and scheduling problem (LSSP) under uncertainty. The model considers economic aspects, reliability and quality inspection, and customer satisfaction and behaviour in designing the LSSP. A utility function is applied to increase customer satisfaction and maximize responsiveness. In addition, the adaptive neuro-fuzzy inference system is employed to address uncertain demands. The presented model uses a fuzzy c-means clustering method to assess customers' behaviour. A hybrid multi-objective metaheuristic algorithm, comprised of the multi-objective red deer algorithm and parallel non-dominated sorting genetic algorithm-II, is applied to solve the model efficiently. The results obtained from experiments on several problem instances show the superiority of the proposed metaheuristic algorithm over other algorithms, such as multi-objective particle swarm optimization, used in this article. Finally, a real case study is presented to show the applicability of the model, and several analyses are implemented to extend managerial insights.