EconPapers    
Economics at your fingertips  
 

Sustainable multi-objective process planning in reconfigurable manufacturing environment: adapted new dynamic NSGA-II vs New NSGA-III

Imen Khettabi, Lyes Benyoucef and Mohamed Amine Boutiche

International Journal of Production Research, 2022, vol. 60, issue 20, 6329-6349

Abstract: The highly competitive and volatile market puts companies in a tough position. While cost and time efficiency are important to stay competitive, environmental awareness is more and more critical. The reconfigurable manufacturing system (RMS) paradigm is suggested to cope with these new challenges. In addition to its six fundamental characteristics, it is seen as an enabler for Industry 4.0. This article investigates the multi-objective process planning problem in an environmentally conscious manner in a reconfigurable manufacturing environment. Four criteria are minimised: total production cost, total production time, total amount of greenhouse gas produced by machines, and total quantity of hazardous liquid wastes. To address the problem, modified versions of the non-dominated sorting genetic algorithm (NSGA) method, namely new dynamic NSGA-II (NewD-NSGA-II) and New NSGA-III, are developed and evaluated. Rich experimental results are presented and analysed using three metrics to demonstrate the efficacy of the proposed approaches: inverted generational distance (IGD), diversity measure (DM), and cardinality of the mixed Pareto fronts (CMPF). The effects of the similarity coefficient on the convergence of the NewD-NSGA-II and New NSGA-III are investigated, and the TOPSIS technique is used to assist the decision-maker in evaluating and selecting the best process plans.

Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2022.2044537 (text/html)
Access to full text is restricted to subscribers.

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:taf:tprsxx:v:60:y:2022:i:20:p:6329-6349

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2022.2044537

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:tprsxx:v:60:y:2022:i:20:p:6329-6349