Manufacturing companies are forced to become energy-aware under the pressure of energy costs, legislation and consumers' environmental awareness. Production scheduling remains a critical decision making process, although demanding in computational terms and sensitive on data availability and credibility. Hence the interest in incorporating energy-related aspects in production scheduling. We propose a decision support system (DSS), composed by an Iterated Local Search algorithm that offers hierarchical optimization over multiple scheduling objectives and is energy-aware in terms of both the constraints incorporated and the objectives to be optimized, plus a generic yet concise data model whose entities are extracted from the literature and actual user requirements. The use of this DSS by two textile manufacturers shows that it supports efficiently energy-aware scheduling decisions.
['Stathis Plitsos', 'Panagiotis P. Repoussis', 'Ioannis Mourtos', 'Christos D. Tarantilis']
Energy-aware decision support for production scheduling