ABSTRACT
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.
Disclosure statement
No potential conflict of interest was reported by the authors.