International Journal of Computational Intelligence Systems

Volume 11, Issue 1, 2018, Pages 805 - 829

Reduction of carbon emission and total late work criterion in job shop scheduling by applying a multi-objective imperialist competitive algorithm

Authors
Hamed Piroozfard1, phamed2@live.utm.my, Kuna Yew Wong1, *, wongky@mail.fkm.utm.my, Manor Kumar Tiara2, mktiwari9@iem.iitkgp.ernet.in
1Department of Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia
2Department of Industrial and Systems Engineering, Indian Institute of Technology, 721302 Kharagpur, India
*Corresponding author
Corresponding Author
Received 27 September 2016, Accepted 16 August 2017, Available Online 1 January 2018.
DOI
10.2991/ijcis.11.1.62How to use a DOI?
Keywords
Job shop scheduling; environmentally sustainable operations management; carbon footprint; late work criterion; multi-objective imperialist competitive algorithm; multi-objective optimization problem
Abstract

New environmental regulations have driven companies to adopt low-carbon manufacturing. This research is aimed at considering carbon dioxide in the operational decision level where limited studies can be found, especially in the scheduling area. In particular, the purpose of this research is to simultaneously minimize carbon emission and total late work criterion as sustainability-based and classical-based objective functions, respectively, in the multi-objective job shop scheduling environment. In order to solve the presented problem more effectively, a new multi-objective imperialist competitive algorithm imitating the behavior of imperialistic competition is proposed to obtain a set of non-dominated schedules. In this work, a three-fold scientific contribution can be observed in the problem and solution method, that are: (1) integrating carbon dioxide into the operational decision level of job shop scheduling, (2) considering total late work criterion in multi-objective job shop scheduling, and (3) proposing a new multi-objective imperialist competitive algorithm for solving the extended multi-objective optimization problem. The elements of the proposed algorithm are elucidated and forty three small and large sized extended benchmarked data sets are solved by the algorithm. Numerical results are compared with two well-known and most representative metaheuristic approaches, which are multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II, in order to evaluate the performance of the proposed algorithm. The obtained results reveal the effectiveness and efficiency of the proposed multi-objective imperialist competitive algorithm in finding high quality non-dominated schedules as compared to the other metaheuristic approaches.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
11 - 1
Pages
805 - 829
Publication Date
2018/01/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.11.1.62How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Hamed Piroozfard
AU  - Kuna Yew Wong
AU  - Manor Kumar Tiara
PY  - 2018
DA  - 2018/01/01
TI  - Reduction of carbon emission and total late work criterion in job shop scheduling by applying a multi-objective imperialist competitive algorithm
JO  - International Journal of Computational Intelligence Systems
SP  - 805
EP  - 829
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.62
DO  - 10.2991/ijcis.11.1.62
ID  - Piroozfard2018
ER  -