A developed framework for sequencing of mixed-model assembly line with customer's satisfaction and heterogeneous workers

Abstract

Goals: We present a multi-objective mathematical model to determine the optimum production sequence of the mixed-model assembly line (MMAL). Maximizing customer satisfaction and minimizing costs are the objectives of the problem. Design / Methodology / Approach: Customers are divided into two clusters of high priority and low priority by k-medoids method. Also, to get closer to the real world, heterogeneous workers are considered. As the actual scale of the problem cannot be solved by an exact method, two meta-heuristic algorithms, namely Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II) are proposed to solve the problem and reach approximate and efficient results in large scale. Results: It observes that this model can plan the customers' orders by considering their satisfaction. Also, comparing the results of these algorithms indicates a slight superiority of the SPEA2 method. Limitations of the investigation: This study is mainly limited by clustering criteria. In the future, more criteria can be considered for analyzing customer behavior and expanding customer clusters. Practical implications: This model can help all manufacturers who use MMAL by providing a Pareto front for deciding between costs and customers' satisfaction. Originality/Value: Applying k-medoids to cluster the customers for better orders management and proposing SPEA2 and NSGA-II for solving the problem are the main novelties of this study.