Distributed-elite local search based on a genetic algorithm for bi-objective job-shop scheduling under time-of-use tariffs

Bobby Kurniawan*, Wen Song, Wei Weng, Shigeru Fujimura

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


The rapid growth of electricity demand has led governments around the world to implement energy-conscious policies, such as time-of-use tariffs. The manufacturing sector can embrace these policies by implementing an innovative scheduling system to reduce its energy consumption. Therefore, this study addresses bi-objective job-shop scheduling with total weighted tardiness and electricity cost minimization under time-of-use tariffs. The problem can be decomposed into two sub-problems, operation sequencing and start time determination. To solve this problem, we propose a distributed-elite local search based on a genetic algorithm that uses local improvement strategies based on the distribution of elites. Specifically, chromosome encoding uses two lines of gene representation corresponding to the operation sequence and start time. We propose a decoding method to obtain a schedule that incorporates operation sequencing and start time. A perturbation scheme to reduce electricity costs was developed. Finally, a local search framework based on the distribution of elites is used to guide the selection of individuals and the determination of perturbation. Comprehensive numerical experiments using benchmark data from the literature demonstrate that the proposed method is more effective than NSGA-II, MOEA/D, and SPEA2. The results presented in this work may be useful for the manufacturing sector to adopt the time-of-use tariffs policy.

Original languageEnglish
Pages (from-to)1581-1595
Number of pages15
JournalEvolutionary Intelligence
Issue number4
Publication statusPublished - 2021 Dec


  • Bi-objective job shop scheduling
  • Electricity cost
  • Genetic algorithm
  • Time-of-use tariffs
  • Total weighted tardiness

ASJC Scopus subject areas

  • Mathematics (miscellaneous)
  • Computer Vision and Pattern Recognition
  • Cognitive Neuroscience
  • Artificial Intelligence


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