Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm |
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Authors: | Min Dai Dunbing Tang Adriana Giret Miguel A. Salido W.D. Li |
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Affiliation: | 1. College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;2. Jiangsu Key Laboratory of Precision and Micro-Manufacturing Technology, Nanjing 210016, China;3. Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, Camino de Vera s/n 46071, Valencia, Spain;4. Faculty of Engineering and Computing, Coventry University, CV15FB UK |
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Abstract: | The traditional production scheduling problem considers performance indicators such as processing time, cost, and quality as optimization objectives in manufacturing systems; however, it does not take energy consumption or environmental impacts completely into account. Therefore, this paper proposes an energy-efficient model for flexible flow shop scheduling (FFS). First, a mathematical model for a FFS problem, which is based on an energy-efficient mechanism, is described to solve multi-objective optimization. Since FFS is well known as a NP-hard problem, an improved, genetic-simulated annealing algorithm is adopted to make a significant trade-off between the makespan and the total energy consumption to implement a feasible scheduling. Finally, a case study of a production scheduling problem for a metalworking workshop in a plant is simulated. The experimental results show that the relationship between the makespan and the energy consumption may be apparently conflicting. In addition, an energy-saving decision is performed in a feasible scheduling. Using the decision method, there could be significant potential for minimizing energy consumption. |
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Keywords: | Flexible flow shop scheduling (FFS) Energy consumption Energy saving Makespan Genetic-simulated annealing algorithm |
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