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.

中文翻译：

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用于满足客户满意度和异类工人的混合模型装配线排序的已开发框架

目标：我们提出了一个多目标数学模型来确定混合模型装配线（MMAL）的最佳生产顺序。问题的目标是最大化客户满意度和最小化成本。设计/方法/方法：通过k型方法将客户分为高优先级和低优先级两个集群。此外，为了更接近现实世界，还考虑了异构工人。由于无法通过精确的方法解决问题的实际规模，因此提出了两种元启发式算法，即强度帕累托进化算法2（SPEA2）和非支配排序遗传算法II（NSGA-II），以解决该问题并达到近似高效地大规模生产。结果：它观察到该模型可以通过考虑客户的满意度来计划他们的订单。也，比较这些算法的结果表明SPEA2方法略有优势。研究的局限性：本研究主要受聚类标准的限制。将来，可以考虑使用更多标准来分析客户行为并扩展客户群。实际意义：该模型可以通过提供Pareto前端来决定成本和客户满意度之间的关系，从而帮助所有使用MMAL的制造商。独创性/价值：应用k-medoids对客户进行聚类以更好地进行订单管理，并提出SPEA2和NSGA-II解决问题是本研究的主要新颖之处。可以考虑使用更多标准来分析客户行为并扩展客户群。实际意义：该模型可以通过提供Pareto前端来决定成本和客户满意度之间的关系，从而帮助所有使用MMAL的制造商。独创性/价值：应用k-medoids对客户进行聚类以更好地进行订单管理，并提出SPEA2和NSGA-II解决问题是本研究的主要新颖之处。可以考虑使用更多标准来分析客户行为并扩展客户群。实际意义：该模型可以通过提供Pareto前端来决定成本和客户满意度之间的关系，从而帮助所有使用MMAL的制造商。独创性/价值：应用k-medoids对客户进行聚类以更好地进行订单管理，并提出SPEA2和NSGA-II解决问题是本研究的主要新颖之处。