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

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 fro...

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Bibliographic Details
Published in:International journal of production research Vol. 53; no. 23; pp. 7071 - 7089
Main Authors: May, Gökan, Stahl, Bojan, Taisch, Marco, Prabhu, Vittal
Format: Journal Article
Language:English
Published: London Taylor & Francis 02.12.2015
Taylor & Francis LLC
Subjects:
Algorithms
Assessments
Energy consumption
Energy efficiency
Energy policy
Genetic algorithms
job shop
Job shop scheduling
Job shops
machine control policies
Machine shops
Production scheduling
scheduling
Studies
Sustainability
sustainable manufacturing
ISSN:0020-7543, 1366-588X
Online Access:Get full text
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Summary: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.
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ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2015.1005248