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Solving a bi-objective mixed-model assembly-line sequencing using metaheuristic algorithms considering ergonomic factors, customer behavior, and periodic maintenance

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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.

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Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors

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Authors and Affiliations

  1. School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Masoud Rabbani, Mahdi Mokhtarzadeh & Azadeh Farsi

  2. Department of Industrial Engineering, KHATAM University, Tehran, Iran

    Neda Manavizadeh

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  1. Masoud Rabbani
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  2. Mahdi Mokhtarzadeh
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  3. Neda Manavizadeh
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  4. Azadeh Farsi
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Correspondence to Masoud Rabbani.

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The data of this research are generated randomly and are available upon request.

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The codes of implemented meta-heuristic algorithm are available at “https://www.dropbox.com/s/ohqw924xgjai2q6/ee.zip?dl=0

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Appendix A

Appendix A

See Table 6 and Fig. 7

Table 6 The selected level of proposed meta-heuristics for parameter tuning
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Fig. 7
figure 7

Analysis diagrams of the Taguchi method for a MOTS, b NSGA-II, and c MOSA parameters tuning

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Rabbani, M., Mokhtarzadeh, M., Manavizadeh, N. et al. Solving a bi-objective mixed-model assembly-line sequencing using metaheuristic algorithms considering ergonomic factors, customer behavior, and periodic maintenance. OPSEARCH 58, 513–539 (2021). https://doi.org/10.1007/s12597-020-00489-y

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