Artikel

Mathematical model for dynamic cell formation in fast fashion apparel manufacturing stage

This paper presents a mathematical programming model for dynamic cell formation to minimize changeover-related costs (i.e., machine relocation costs and machine setup cost) and inter-cell material handling cost to cope with the volatile production environments in apparel manufacturing industry. The model is formulated through findings of a comprehensive literature review. Developed model is validated based on data collected from three different factories in apparel industry, manufacturing fast fashion products. A program code is developed using Lingo 16.0 software package to generate optimal cells for developed model and to determine the possible cost-saving percentage when the existing layouts used in three factories are replaced by generated optimal cells. The optimal cells generated by developed mathematical model result in significant cost saving when compared with existing product layouts used in production/assembly department of selected factories in apparel industry. The developed model can be considered as effective in minimizing the considered cost terms in dynamic production environment of fast fashion apparel manufacturing industry. Findings of this paper can be used for further researches on minimizing the changeover-related costs in fast fashion apparel production stage.

Language
Englisch

Bibliographic citation
Journal: Journal of Industrial Engineering International ; ISSN: 2251-712X ; Volume: 15 ; Year: 2019 ; Issue: 1 ; Pages: 1-16 ; Heidelberg: Springer

Classification
Management
Subject
Dynamic cell
Labor-intensive
Apparel
Product layout
Changeover
Cost saving

Event
Geistige Schöpfung
(who)
Perera, Gayathri
Ratnayake, Vijitha
Event
Veröffentlichung
(who)
Springer
(where)
Heidelberg
(when)
2019

DOI
doi:10.1007/s40092-018-0275-5
Handle
Last update
10.03.2025, 11:43 AM CET

Data provider

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Object type

  • Artikel

Associated

  • Perera, Gayathri
  • Ratnayake, Vijitha
  • Springer

Time of origin

  • 2019

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