An effective multi-objective whale swarm algorithm for energy-efficient scheduling of distributed welding flow shop

Guangchen Wang (), Xinyu Li (), Liang Gao () and Peigen Li ()
Additional contact information
Guangchen Wang: Huazhong University of Science and Technology
Xinyu Li: Huazhong University of Science and Technology
Liang Gao: Huazhong University of Science and Technology
Peigen Li: Huazhong University of Science and Technology

Annals of Operations Research, 2022, vol. 310, issue 1, No 10, 223-255

Abstract: Abstract Distributed welding flow shop scheduling problem is an extension of distributed permutation flow shop scheduling problem, which possesses a set of identical factories of welding flow shop. On account of several machines can process one job simultaneously in welding shop, increasing the amount of machines can short the processing time of operation while waste more energy consumption at the same time. Thus, energy-efficient is of great significance to take total energy consumption into account in scheduling. A multi-objective mixed integer programming model for energy-efficient scheduling of distributed welding flow shop is presented based on three sub-problems with allocating jobs among factories, scheduling the jobs in each factory and determining the amount of machines upon each job. A multi-objective whale swarm algorithm is proposed to optimize the total energy consumption and makespan simultaneously. In the proposed algorithm, a new initialization method is designed to improve the quality of the initial solution. And various update operators, as well as local search, are designed according to the feature of the problem. To conduct the experiment, diversified indicators are applied to evaluate the proposed algorithm and other MOEAs performance. And the experiment results demonstrate the effectiveness of the proposed method. The proposed algorithm is applied in the real-life case with great performance compared with other MOEAs.

Keywords: Distributed welding flow shop; Energy-efficient scheduling; Whale swarm algorithm; Multi-objective optimization (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s10479-021-03952-1 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:annopr:v:310:y:2022:i:1:d:10.1007_s10479-021-03952-1

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-021-03952-1

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

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