A Developed Framework for Sequencing of Mixed-model Assembly Line with Customer’s Satisfaction and Heterogeneous Operators

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 metaheuristic 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.