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Energy-efficient approach to minimizing the energy consumption in an extended job-shop scheduling problem

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Abstract

The traditional production planning and scheduling problems consider performance indicators like time, cost and quality as optimization objectives in manufacturing processes. However, environmentally-friendly factors like energy consumption of production have not been completely taken into consideration. Against this background, this paper addresses an approach to modify a given schedule generated by a production planning and scheduling system in a job shop floor, where machine tools can work at different cutting speeds. It can adjust the cutting speeds of the operations while keeping the original assignment and processing sequence of operations of each job fixed in order to obtain energy savings. First, the proposed approach, based on a mixed integer programming mathematical model, changes the total idle time of the given schedule to minimize energy consumption in the job shop floor while accepting the optimal solution of the scheduling objective, makespan. Then, a genetic-simulated annealing algorithm is used to explore the optimal solution due to the fact that the problem is strongly NP-hard. Finally, the effectiveness of the approach is performed smalland large-size instances, respectively. The experimental results show that the approach can save 5%–10% of the average energy consumption while accepting the optimal solution of the makespan in small-size instances. In addition, the average maximum energy saving ratio can reach to 13%. And it can save approximately 1%–4% of the average energy consumption and approximately 2.4% of the average maximum energy while accepting the near-optimal solution of the makespan in large-size instances. The proposed research provides an interesting point to explore an energy-aware schedule optimization for a traditional production planning and scheduling problem.

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, 210016, China

    Dunbing Tang & Min Dai

  2. Jiangsu Key Laboratory of Precision and Micro-manufacturing Technology, Nanjing University of Aeronautics & Astronautics, Nanjing, 210016, China

    Dunbing Tang & Min Dai

Authors
  1. Dunbing Tang
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  2. Min Dai
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Corresponding author

Correspondence to Dunbing Tang.

Additional information

Supported by a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Program (Grant No. 294931), National Science Foundation of China (Grant No. 51175262), Jiangsu Provincial Science Foundation for Excellent Youths of China (Grant No. BK2012032), and Jiangsu Provincial Industry-Academy-Research Grant of China (Grant No. BY201220116)

TANG Dunbing, born in 1972, is a professor at Nanjing University of Aeronautics & Astronautics, China. He received his PhD from Nanjing University of Science and Technology, China, in 2000. His research interests include engineering design and intelligent manufacturing system modeling.

DAI Min, born in 1987, is currently a PhD candidate at College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics & Astronautics, China. He received his bachelor degree from Yangzhou University, China, in 2011. His research interests include applications of heuristic optimization algorithm in production scheduling and intelligent manufacturing system modeling.

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Tang, D., Dai, M. Energy-efficient approach to minimizing the energy consumption in an extended job-shop scheduling problem. Chin. J. Mech. Eng. 28, 1048–1055 (2015). https://doi.org/10.3901/CJME.2015.0617.082

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  • DOI: https://doi.org/10.3901/CJME.2015.0617.082

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