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

研究成果: Conference contribution

3 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
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 102017 12 13

出版物シリーズ

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

Other

Other2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017
CountrySingapore
CitySingapore
Period17/12/1017/12/13

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality

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