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Triple‐chromosome genetic algorithm for unrelated parallel machine scheduling under time‐of‐use tariffs
Author(s) -
Kurniawan Bobby,
Chandramitasari Widyaning,
Gozali Alfian Akbar,
Weng Wei,
Fujimura Shigeru
Publication year - 2020
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.23047
Subject(s) - crossover , job shop scheduling , chromosome , computer science , mathematical optimization , genetic algorithm , scheduling (production processes) , population , job scheduler , mathematics , genetics , artificial intelligence , biology , medicine , gene , schedule , environmental health , queue , programming language , operating system
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. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.