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

• 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; 1366-588X
• DOI: 10.1080/00207543.2015.1005248

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.