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

Masoud Rabbani (), Mahdi Mokhtarzadeh, Neda Manavizadeh and Azadeh Farsi
Additional contact information
Masoud Rabbani: University of Tehran
Mahdi Mokhtarzadeh: University of Tehran
Neda Manavizadeh: KHATAM University
Azadeh Farsi: University of Tehran

OPSEARCH, 2021, vol. 58, issue 3, No 1, 513-539

Abstract: 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 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12597-020-00489-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:opsear:v:58:y:2021:i:3:d:10.1007_s12597-020-00489-y

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/12597

DOI: 10.1007/s12597-020-00489-y

Access Statistics for this article

OPSEARCH is currently edited by Birendra Mandal

More articles in OPSEARCH from Springer, Operational Research Society of India
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().