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Research on Energy-Saving Production Scheduling Based on a Clustering Algorithm for a Forging Enterprise

Author

Listed:
  • Yifei Tong

    (School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
    These authors contributed equally to this work.)

  • Jingwei Li

    (School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
    These authors contributed equally to this work.)

  • Shai Li

    (School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
    These authors contributed equally to this work.)

  • Dongbo Li

    (School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
    These authors contributed equally to this work.)

Abstract

Energy efficiency is a buzzword of the 21st century. With the ever growing need for energy efficient and low-carbon production, it is a big challenge for high energy-consumption enterprises to reduce their energy consumption. To this aim, a forging enterprise, DVR (the abbreviation of a forging enterprise), is researched. Firstly, an investigation into the production processes of DVR is given as well as an analysis of forging production. Then, the energy-saving forging scheduling is decomposed into two sub-problems. One is for cutting and machining scheduling, which is similar to traditional machining scheduling. The other one is for forging and heat treatment scheduling. Thirdly, former forging production scheduling is presented and solved based on an improved genetic algorithm. Fourthly, the latter is discussed in detail, followed by proposed dynamic clustering and stacking combination optimization. The proposed stacking optimization requires making the gross weight of forgings as close to the maximum batch capacity as possible. The above research can help reduce the heating times, and increase furnace utilization with high energy efficiency and low carbon emissions.

Suggested Citation

  • Yifei Tong & Jingwei Li & Shai Li & Dongbo Li, 2016. "Research on Energy-Saving Production Scheduling Based on a Clustering Algorithm for a Forging Enterprise," Sustainability, MDPI, vol. 8(2), pages 1-17, February.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:2:p:136-:d:63260
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    References listed on IDEAS

    as
    1. Chang, Pei-Chann & Huang, Wei-Hsiu & Wu, Jheng-Long & Cheng, T.C.E., 2013. "A block mining and re-combination enhanced genetic algorithm for the permutation flowshop scheduling problem," International Journal of Production Economics, Elsevier, vol. 141(1), pages 45-55.
    2. Rosario Domingo & Sergio Aguado, 2015. "Overall Environmental Equipment Effectiveness as a Metric of a Lean and Green Manufacturing System," Sustainability, MDPI, vol. 7(7), pages 1-17, July.
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    Cited by:

    1. Fei He & Kang Shen & Li Guan & Mingming Jiang, 2017. "Research on Energy-Saving Scheduling of a Forging Stock Charging Furnace Based on an Improved SPEA2 Algorithm," Sustainability, MDPI, vol. 9(11), pages 1-21, November.
    2. Sang-Oh Shim & KyungBae Park & SungYong Choi, 2017. "Innovative Production Scheduling with Customer Satisfaction Based Measurement for the Sustainability of Manufacturing Firms," Sustainability, MDPI, vol. 9(12), pages 1-12, December.
    3. Juan Pablo Usuga Cadavid & Samir Lamouri & Bernard Grabot & Robert Pellerin & Arnaud Fortin, 2020. "Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1531-1558, August.
    4. Rui Zhang, 2017. "Sustainable Scheduling of Cloth Production Processes by Multi-Objective Genetic Algorithm with Tabu-Enhanced Local Search," Sustainability, MDPI, vol. 9(10), pages 1-26, September.
    5. Sang-Oh Shim & KyungBae Park, 2016. "Technology for Production Scheduling of Jobs for Open Innovation and Sustainability with Fixed Processing Property on Parallel Machines," Sustainability, MDPI, vol. 8(9), pages 1-10, September.
    6. Chen Peng & Tao Peng & Yi Zhang & Renzhong Tang & Luoke Hu, 2018. "Minimising Non-Processing Energy Consumption and Tardiness Fines in a Mixed-Flow Shop," Energies, MDPI, vol. 11(12), pages 1-15, December.

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