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.

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

MAY, GÖKAN;STAHL, BOJAN;TAISCH, MARCO;
2015-01-01

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.
energy efficiency; genetic algorithms; job shop; machine control policies; scheduling; sustainable manufacturing; Industrial and Manufacturing Engineering; Management Science and Operations Research; Strategy and Management1409 Tourism, Leisure and Hospitality Management
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/979255
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