A novel approach to determine the cell formation using heuristics approach

Shruti Shashikumar (), Rakesh D. Raut (), Vaibhav S. Narwane (), Bhaskar B. Gardas (), Balkrishna E. Narkhede () and Anjali Awasthi ()
Additional contact information
Shruti Shashikumar: K.J. Somaiya College of Engineering
Rakesh D. Raut: National Institute of Industrial Engineering (NITIE)
Vaibhav S. Narwane: Veermata Jijabai Technological Institute (VJTI)
Bhaskar B. Gardas: Veermata Jijabai Technological Institute (VJTI)
Balkrishna E. Narkhede: National Institute of Industrial Engineering (NITIE)
Anjali Awasthi: Concordia University

OPSEARCH, 2019, vol. 56, issue 3, No 2, 628-656

Abstract: Abstract Cellular manufacturing is a vital part of lean manufacturing. It is an application of group technology. Three problems in cellular manufacturing are cell formation, machine layout and cell layout problems. However, these problems are NP-hard optimisation problems and cannot be solved using exact methods. A difficult part is to form the machine groups or cells, also called Cell Formation Problem and several techniques have been proposed to solve the same. In this paper, the Cell Formation Problem is solved using an integrated approach of heuristics along with Genetic Algorithm and Membership Index. Heuristics technique is used for domain selection which is used in Genetic Algorithm as the initial population. Genetic Algorithm is useful for optimising the results of machine assignment to cells, and Membership Index is used to assign parts to the cells. The performance is analysed using performance measures such as group technology efficiency and some exceptional elements. The proposed computational methodology is tested on standard problems of diverse size from literature papers using the hybrid approach. Results from test problems show that the proposed method is effective and efficient. The paper is useful from the practicality aspect and also relevant from current research and industry trends.

Keywords: Cell formation; Cellular manufacturing; Genetic algorithm; Heuristics; Membership index; Hybrid approach (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12597-019-00381-4 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:56:y:2019:i:3:d:10.1007_s12597-019-00381-4

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

DOI: 10.1007/s12597-019-00381-4

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 ().