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

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.

Keywords:

• energy efficiency
• job shop
• scheduling
• genetic algorithms
• machine control policies
• sustainable manufacturing

Acknowledgements

The authors thank the editor and two anonymous reviewers for their insightful criticisms, encouragement and helpful suggestions, which greatly improved this paper. We would like to thank also our colleague Fabrizio Chiesa for his support in the research. A special thanks goes to our British colleague Emmanuel Damilola Lawal for reading and editing the manuscript.