EconPapers    
Economics at your fingertips  
 

A hyper-heuristic approach for stochastic parallel assembly line balancing problems with equipment costs

Lale Özbakır () and Gökhan Seçme ()
Additional contact information
Lale Özbakır: Erciyes University
Gökhan Seçme: Nevşehir Hacı Bektaş Veli University

Operational Research, 2022, vol. 22, issue 1, No 20, 577-614

Abstract: Abstract This study addresses the stochastic parallel assembly line balancing problem with equipment costs and presents a hyper-heuristic approach based on simulated annealing for solving it. A cost-based objective function is employed to represent the incompletion, equipment, and station installation costs. The hyper-heuristic approach is utilized to search on sequencing heuristics search space, rather than a problem-specific solution space. This study focuses on the consideration of equipment costs while balancing a stochastic parallel assembly line. The performance of the solution approach is also tested on the single-model stochastic assembly line balancing problems and stochastic parallel assembly line balancing problems due to the generalizability of hyper-heuristics. The results of the benchmark problems show that in most cases the proposed algorithm provides better solutions than the best-known solutions in literature. An extensive computational study performed to determine the parameter levels derived from the problem and the solution method. The effect of the equipment costs for stochastic parallel assembly lines is also analyzed in detail.

Keywords: Hyper-heuristics; Stochastic parallel assembly line balancing; Equipment costs; Simulated annealing (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12351-020-00561-x 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:operea:v:22:y:2022:i:1:d:10.1007_s12351-020-00561-x

Ordering information: This journal article can be ordered from
https://www.springer ... search/journal/12351

DOI: 10.1007/s12351-020-00561-x

Access Statistics for this article

Operational Research is currently edited by Nikolaos F. Matsatsinis, John Psarras and Constantin Zopounidis

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

 
Page updated 2025-03-20
Handle: RePEc:spr:operea:v:22:y:2022:i:1:d:10.1007_s12351-020-00561-x