A genetic algorithm for unrelated parallel machine scheduling minimizing makespan cost and electricity cost under time-of-use (TOU) tariffs with job delay mechanism

B. Kurniawan; A. A. Gozali; W. Weng; S. Fujimura

doi:10.1109/IEEM.2017.8289958

A genetic algorithm for unrelated parallel machine scheduling minimizing makespan cost and electricity cost under time-of-use (TOU) tariffs with job delay mechanism

B. Kurniawan, A. A. Gozali, W. Weng, S. Fujimura

大学院情報生産システム研究科

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

Unrelated parallel machine scheduling under time-of-use electricity price is addressed in this paper. In this setting, price of electricity can be different among various periods of the day. The objective is to minimize total cost consisting of makespan cost and electricity cost. Genetic algorithm (GA) is used to solve the unrelated parallel machine scheduling under time varying tariffs. Chromosome decoding, inspired by greedy total cost, is proposed to transform individual into feasible schedule. Furthermore, generated schedule from the individual is improved by job delay mechanism that shifts jobs to other periods to avoid high electricity cost. Finally, numerical experiment is conducted to implement the approach. Preliminary result shows that our proposed approach is effective and efficient to solve the corresponding problem.

本文言語	English
ホスト出版物のタイトル	2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017
出版社	IEEE Computer Society
ページ	583-587
ページ数	5
ISBN（電子版）	9781538609484
DOI	https://doi.org/10.1109/IEEM.2017.8289958
出版ステータス	Published - 2018 2 9
イベント	2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017 - Singapore, Singapore 継続期間: 2017 12 10 → 2017 12 13

出版物シリーズ

名前	IEEE International Conference on Industrial Engineering and Engineering Management
巻	2017-December
ISSN（印刷版）	2157-3611
ISSN（電子版）	2157-362X

Other

Other	2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017
国/地域	Singapore
City	Singapore
Period	17/12/10 → 17/12/13

ASJC Scopus subject areas

ビジネス、管理および会計（その他）
産業および生産工学
安全性、リスク、信頼性、品質管理

文献へのアクセス

10.1109/IEEM.2017.8289958

他のファイルとリンク

フィンガープリント

「A genetic algorithm for unrelated parallel machine scheduling minimizing makespan cost and electricity cost under time-of-use (TOU) tariffs with job delay mechanism」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル

Kurniawan, B., Gozali, A. A., Weng, W., & Fujimura, S. (2018). A genetic algorithm for unrelated parallel machine scheduling minimizing makespan cost and electricity cost under time-of-use (TOU) tariffs with job delay mechanism. In 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017 (pp. 583-587). (IEEE International Conference on Industrial Engineering and Engineering Management; Vol. 2017-December). IEEE Computer Society. https://doi.org/10.1109/IEEM.2017.8289958

A genetic algorithm for unrelated parallel machine scheduling minimizing makespan cost and electricity cost under time-of-use (TOU) tariffs with job delay mechanism. / Kurniawan, B.; Gozali, A. A.; Weng, W.; Fujimura, S.

2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017. IEEE Computer Society, 2018. p. 583-587 (IEEE International Conference on Industrial Engineering and Engineering Management; Vol. 2017-December).

研究成果: Conference contribution

Kurniawan, B, Gozali, AA, Weng, W & Fujimura, S 2018, A genetic algorithm for unrelated parallel machine scheduling minimizing makespan cost and electricity cost under time-of-use (TOU) tariffs with job delay mechanism. in 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017. IEEE International Conference on Industrial Engineering and Engineering Management, vol. 2017-December, IEEE Computer Society, pp. 583-587, 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017, Singapore, Singapore, 17/12/10. https://doi.org/10.1109/IEEM.2017.8289958

Kurniawan B, Gozali AA, Weng W, Fujimura S. A genetic algorithm for unrelated parallel machine scheduling minimizing makespan cost and electricity cost under time-of-use (TOU) tariffs with job delay mechanism. In 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017. IEEE Computer Society. 2018. p. 583-587. (IEEE International Conference on Industrial Engineering and Engineering Management). https://doi.org/10.1109/IEEM.2017.8289958

Kurniawan, B. ; Gozali, A. A. ; Weng, W. ; Fujimura, S. / A genetic algorithm for unrelated parallel machine scheduling minimizing makespan cost and electricity cost under time-of-use (TOU) tariffs with job delay mechanism. 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017. IEEE Computer Society, 2018. pp. 583-587 (IEEE International Conference on Industrial Engineering and Engineering Management).

@inproceedings{df3650a126ce4c51b13dec6ff0bd2ee9,

title = "A genetic algorithm for unrelated parallel machine scheduling minimizing makespan cost and electricity cost under time-of-use (TOU) tariffs with job delay mechanism",

abstract = "Unrelated parallel machine scheduling under time-of-use electricity price is addressed in this paper. In this setting, price of electricity can be different among various periods of the day. The objective is to minimize total cost consisting of makespan cost and electricity cost. Genetic algorithm (GA) is used to solve the unrelated parallel machine scheduling under time varying tariffs. Chromosome decoding, inspired by greedy total cost, is proposed to transform individual into feasible schedule. Furthermore, generated schedule from the individual is improved by job delay mechanism that shifts jobs to other periods to avoid high electricity cost. Finally, numerical experiment is conducted to implement the approach. Preliminary result shows that our proposed approach is effective and efficient to solve the corresponding problem.",

keywords = "Genetic algorithm, electricity cost, makespan cost, time-of-use tariffs, unrelated parallel machine scheduling with job delay mechanism",

author = "B. Kurniawan and Gozali, {A. A.} and W. Weng and S. Fujimura",

note = "Funding Information: This research is supported by Indonesia Endowment Fund for Education (LPDP). Publisher Copyright: {\textcopyright} 2017 IEEE. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.; 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017 ; Conference date: 10-12-2017 Through 13-12-2017",

year = "2018",

month = feb,

day = "9",

doi = "10.1109/IEEM.2017.8289958",

language = "English",

series = "IEEE International Conference on Industrial Engineering and Engineering Management",

publisher = "IEEE Computer Society",

pages = "583--587",

booktitle = "2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017",

}

TY - GEN

T1 - A genetic algorithm for unrelated parallel machine scheduling minimizing makespan cost and electricity cost under time-of-use (TOU) tariffs with job delay mechanism

AU - Kurniawan, B.

AU - Gozali, A. A.

AU - Weng, W.

AU - Fujimura, S.

PY - 2018/2/9

Y1 - 2018/2/9

N2 - Unrelated parallel machine scheduling under time-of-use electricity price is addressed in this paper. In this setting, price of electricity can be different among various periods of the day. The objective is to minimize total cost consisting of makespan cost and electricity cost. Genetic algorithm (GA) is used to solve the unrelated parallel machine scheduling under time varying tariffs. Chromosome decoding, inspired by greedy total cost, is proposed to transform individual into feasible schedule. Furthermore, generated schedule from the individual is improved by job delay mechanism that shifts jobs to other periods to avoid high electricity cost. Finally, numerical experiment is conducted to implement the approach. Preliminary result shows that our proposed approach is effective and efficient to solve the corresponding problem.

AB - Unrelated parallel machine scheduling under time-of-use electricity price is addressed in this paper. In this setting, price of electricity can be different among various periods of the day. The objective is to minimize total cost consisting of makespan cost and electricity cost. Genetic algorithm (GA) is used to solve the unrelated parallel machine scheduling under time varying tariffs. Chromosome decoding, inspired by greedy total cost, is proposed to transform individual into feasible schedule. Furthermore, generated schedule from the individual is improved by job delay mechanism that shifts jobs to other periods to avoid high electricity cost. Finally, numerical experiment is conducted to implement the approach. Preliminary result shows that our proposed approach is effective and efficient to solve the corresponding problem.

KW - Genetic algorithm

KW - electricity cost

KW - makespan cost

KW - time-of-use tariffs

KW - unrelated parallel machine scheduling with job delay mechanism

UR - http://www.scopus.com/inward/record.url?scp=85045272386&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85045272386&partnerID=8YFLogxK

U2 - 10.1109/IEEM.2017.8289958

DO - 10.1109/IEEM.2017.8289958

M3 - Conference contribution

AN - SCOPUS:85045272386

T3 - IEEE International Conference on Industrial Engineering and Engineering Management

SP - 583

EP - 587

BT - 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017

PB - IEEE Computer Society

T2 - 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017

Y2 - 10 December 2017 through 13 December 2017

ER -