Solving a bi-objective mixed-model assembly-line sequencing using metaheuristic algorithms considering ergonomic factors
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Solving a bi‑objective mixed‑model assembly‑line sequencing using metaheuristic algorithms considering ergonomic factors, customer behavior, and periodic maintenance Masoud Rabbani1 · Mahdi Mokhtarzadeh1 · Neda Manavizadeh2 · Azadeh Farsi1 Accepted: 1 November 2020 © Operational Research Society of India 2020
Abstract In today’s competitive world, companies must maintain their customers and attract new ones. Hence, they paid a great attention paid to mixed model assembly lines (MMAL). In this study, a two-step framework was developed to investigate and optimize customer relationships and the sequence of orders in an MMAL. First, based on customers past behavior, they were grouped into three clusters with high, normal, and low priority. Then, an optimal sequence was defined using a mathematical model. The objectives of the sequence were maximizing, first, the satisfaction of customers with high priority and, second, profits. Moreover, orders for low priority customers could be rejected. A multi-objective tabu search algorithm was proposed to solve the sequencing problem and then compared with non-dominated sorting genetic algorithm II and multi objective simulated annealing. The results indicated that this new algorithm is superior to others. We also developed an algorithm for the integration of periodic maintenance with sequencing of orders. The results suggested that the lack of this integration causes non-optimal sequences. Keywords Customers clustering · K-means · LRFMP · Mixed-model assembly lines · Periodic maintenance · Sequencing
1 Introduction Increasing diversity in customer demands has become a major challenge for manufacturers. In this context, a lot of attention has been devoted to mixed-model assembly lines (MMALs) that are used in most manufacturing companies [7]. MMALs * Masoud Rabbani [email protected] 1
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
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Department of Industrial Engineering, KHATAM University, Tehran, Iran
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are used to deal with a variety of orders by assembling different models on a single assembly line, which enables manufacturers to tackle diversity in customer demands. MMALs are widely applicable in various industries, including automotive industry, white goods, clothing, and furniture [6]. In an MMAL, the correct sequencing of workpieces has an enormous impact on the performance of the system. In this regard, [7] conducted a complete review of some fundamental approaches like mixed-model sequencing, level scheduling, and care sequencing. They mentioned minimizing work overload and leveling part usage as two basic and common objectives of MMAL sequencing in the literature. Meanwhile, more recent studies have proposed other objectives like maximizing customer satisfaction [32] and minimizing total idle time cost [28]. Various studies have focused on the development of MMAL sequencing in recent years. Thus, Akgündüz and Tunalı 1] developed an adaptive genetic algorithm (GA) to solve an MMAL seque
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