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

Graduate School of Information, Production and Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

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.

Original language	English
Title of host publication	2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017
Publisher	IEEE Computer Society
Pages	583-587
Number of pages	5
ISBN (Electronic)	9781538609484
DOIs	https://doi.org/10.1109/IEEM.2017.8289958
Publication status	Published - 2018 Feb 9
Event	2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017 - Singapore, Singapore Duration: 2017 Dec 10 → 2017 Dec 13

Publication series

Name	IEEE International Conference on Industrial Engineering and Engineering Management
Volume	2017-December
ISSN (Print)	2157-3611
ISSN (Electronic)	2157-362X

Other

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

Keywords

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

ASJC Scopus subject areas

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

Access to Document

10.1109/IEEM.2017.8289958

Cite this

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).

Research output: Chapter in Book/Report/Conference proceeding › 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).

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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.",

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

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Keywords

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