TY - JOUR
T1 - Triple-chromosome genetic algorithm for unrelated parallel machine scheduling under time-of-use tariffs
AU - Kurniawan, Bobby
AU - Chandramitasari, Widyaning
AU - Gozali, Alfian Akbar
AU - Weng, Wei
AU - Fujimura, Shigeru
N1 - Funding Information:
The authors want to thank for anonymous referees to give constructive feedback. The authors also thank to all persons who have supported this research and cannot be mentioned one by one. This research is supported by the Indonesia Endowment Fund for Education (Lembaga Pengelola Dana Pendidikan or LPDP Indonesia).
Publisher Copyright:
© 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
PY - 2020/2/1
Y1 - 2020/2/1
N2 - Energy demand is increasing as the population and economy grow. Many countries have implemented time-of-use (TOU) tariffs to meet such demand so that the demand during peak periods could be reduced by shifting its usage from peak periods to off-peak periods. This paper addresses the unrelated parallel machine scheduling under TOU to minimize the sum of weighted makespan and electricity cost. Because the problem has nonregular performance measure, delaying the starting time of the job can produce a better solution. Hence, not only do we determine the job sequencing and the job assignment, but also we determine the starting time of the job. We propose a triple-chromosome genetic algorithm that represents the job sequencing, the job assignment and the optimal starting time of the job simultaneously. A self-adaptive algorithm is developed to determine the value of the third chromosome after crossover and mutation process. Numerical experiment and statistical analysis are conducted to show the appropriateness and efficacy of the proposed approach.
AB - Energy demand is increasing as the population and economy grow. Many countries have implemented time-of-use (TOU) tariffs to meet such demand so that the demand during peak periods could be reduced by shifting its usage from peak periods to off-peak periods. This paper addresses the unrelated parallel machine scheduling under TOU to minimize the sum of weighted makespan and electricity cost. Because the problem has nonregular performance measure, delaying the starting time of the job can produce a better solution. Hence, not only do we determine the job sequencing and the job assignment, but also we determine the starting time of the job. We propose a triple-chromosome genetic algorithm that represents the job sequencing, the job assignment and the optimal starting time of the job simultaneously. A self-adaptive algorithm is developed to determine the value of the third chromosome after crossover and mutation process. Numerical experiment and statistical analysis are conducted to show the appropriateness and efficacy of the proposed approach.
KW - genetic algorithm
KW - self-adaptive
KW - time-of-use electricity tariffs
KW - triple-chromosome
KW - unrelated parallel machine
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U2 - 10.1002/tee.23047
DO - 10.1002/tee.23047
M3 - Article
AN - SCOPUS:85075284123
VL - 15
SP - 208
EP - 217
JO - IEEJ Transactions on Electrical and Electronic Engineering
JF - IEEJ Transactions on Electrical and Electronic Engineering
SN - 1931-4973
IS - 2
ER -