Multi-objective genetic algorithm for energy-efficient job shop scheduling

Gökan May, Bojan Stahl, Marco Taisch, Vittaldas V. Prabhu

Research output: Contribution to journalArticle

73 Citations (Scopus)

Abstract

The paper investigates the effects of production scheduling policies aimed towards improving productive and environmental performances in a job shop system. A green genetic algorithm allows the assessment of multi-objective problems related to sustainability. Two main considerations have emerged from the application of the algorithm. First, the algorithm is able to achieve a semi-optimal makespan similar to that obtained by the best of other methods but with a significantly lower total energy consumption. Second, the study demonstrated that the worthless energy consumption can be reduced significantly by employing complex energy-efficient machine behaviour policies.

Original languageEnglish (US)
Pages (from-to)7071-7089
Number of pages19
JournalInternational Journal of Production Research
Volume53
Issue number23
DOIs
StatePublished - Dec 2 2015

Fingerprint

Energy utilization
Genetic algorithms
Sustainable development
Scheduling
Job shop scheduling
Multi-objective genetic algorithm
Energy consumption
Energy
Environmental performance
Genetic algorithm
Makespan
Production/scheduling
Sustainability
Job shop

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

Cite this

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Multi-objective genetic algorithm for energy-efficient job shop scheduling. / May, Gökan; Stahl, Bojan; Taisch, Marco; Prabhu, Vittaldas V.

In: International Journal of Production Research, Vol. 53, No. 23, 02.12.2015, p. 7071-7089.

Research output: Contribution to journalArticle

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