Multi-objective-based differential evolution for balancing production cost, diversity and aggregated performance attributes in product family design

Ismail M. Ali (), Hasan H. Turan (), Ripon K. Chakrabortty () and Sondoss Elsawah ()
Additional contact information
Ismail M. Ali: University of New South Wales
Hasan H. Turan: University of New South Wales
Ripon K. Chakrabortty: University of New South Wales
Sondoss Elsawah: University of New South Wales

Flexible Services and Manufacturing Journal, 2024, vol. 36, issue 1, No 7, 175-223

Abstract: Abstract In product family design (PFD), deciding on a platform design strategy can be viewed as a multidisciplinary optimization problem that involves several factors, such as design variables, manufacturing costs, customizability, supplier reliability, and customer satisfaction. In this study, a multi-objective based differential evolution (MO-based DE) algorithm has been proposed for tackling the module-based PFD problem. The MO-based DE aims to find the best balance between many objectives, such as total production cost, diversity index, and a combination of other objectives (performance attributes). These objectives include commonality, modularity, and suppliers' reliability and all are aggregated to provide a goodness score. To effectively improve the DE's efficiency while solving such a complex optimization problem, the proposed DE integrates new elements such as (i) a novel solution representation, (ii) an improved heuristic technique for platform development, (iii) a weighted aggregation to combine different objectives, and (iv) a proposed platform-based crossover. To validate its performance, the proposed MO-based DE has been compared with (1) the standard DE to assess the effect of the incorporated new elements on DE’s performance, and (2) well-known fast non-dominant sorting genetic algorithms NSGA-II and (3) NSGA-III for solving a real case study of a family of kettles. The experimental results confirmed the efficacy of the proposed MO-based DE as follows: in terms of average cost value, MO-based DE outperformed standard DE and NSGA-II by 26.40% and 11.69%, respectively. While in terms of goodness score, it achieved 20.69% and 8.05% better scores compared to standard DE and NSGA-II, respectively. Moreover, the proposed MO-based DE attained a very competitive performance against NSGA-III as it reached a better average cost and goodness score of 1.74% and 0.82%, respectively.

Keywords: Product family design; Differential evolution; Multi-objective optimization problems; Supplier’ reliability; Production cost (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10696-022-09480-9 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:flsman:v:36:y:2024:i:1:d:10.1007_s10696-022-09480-9

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10696

DOI: 10.1007/s10696-022-09480-9

Access Statistics for this article

Flexible Services and Manufacturing Journal is currently edited by Hans Günther

More articles in Flexible Services and Manufacturing Journal from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().