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Minimising Non-Processing Energy Consumption and Tardiness Fines in a Mixed-Flow Shop

Author

Listed:
  • Chen Peng

    (Institute of Industrial Engineering, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Tao Peng

    (Institute of Industrial Engineering, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Yi Zhang

    (College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310027, China)

  • Renzhong Tang

    (Institute of Industrial Engineering, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Luoke Hu

    (Institute of Industrial Engineering, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

To meet the increasingly diversified demand of customers, more mixed-flow shops are employed. The flexibility of mixed-flow shops increases the difficulty of scheduling. In this paper, a mixed-flow shop scheduling approach (MFSS) is proposed to minimise the energy consumption and tardiness fine (TF) of production with a special focus on non-processing energy (NPE) reduction. The proposed approach consists of two parts: firstly, a mathematic model is developed to describe how NPE and TF can be determined with a specific schedule; then, a multi-objective evolutionary algorithm with multi-chromosomes (MCEAs) is developed to obtain the optimal solutions considering the NPE-TF trade-offs. A deterministic search method with boundary (DSB) and a non-dominated sorting genetic algorithm (NSGA) are employed to validate the developed MCEA. Finally, a case study on an extrusion die mixed-flow shop is performed to demonstrate the proposed approach in industrial practice. Compared with three traditional scheduling approaches, the better performance of the MFSS in terms of computational time and solution quality could be demonstrated.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3382-:d:187389
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    References listed on IDEAS

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    1. Gökan May & Bojan Stahl & Marco Taisch & Vittal Prabhu, 2015. "Multi-objective genetic algorithm for energy-efficient job shop scheduling," International Journal of Production Research, Taylor & Francis Journals, vol. 53(23), pages 7071-7089, December.
    2. 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.
    3. Bierwirth, C. & Kuhpfahl, J., 2017. "Extended GRASP for the job shop scheduling problem with total weighted tardiness objective," European Journal of Operational Research, Elsevier, vol. 261(3), pages 835-848.
    4. Hu, Luoke & Peng, Chen & Evans, Steve & Peng, Tao & Liu, Ying & Tang, Renzhong & Tiwari, Ashutosh, 2017. "Minimising the machining energy consumption of a machine tool by sequencing the features of a part," Energy, Elsevier, vol. 121(C), pages 292-305.
    5. Liu, Ying & Dong, Haibo & Lohse, Niels & Petrovic, Sanja, 2016. "A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance," International Journal of Production Economics, Elsevier, vol. 179(C), pages 259-272.
    6. S. M. Mousavi & I. Mahdavi & J. Rezaeian & M. Zandieh, 2018. "An efficient bi-objective algorithm to solve re-entrant hybrid flow shop scheduling with learning effect and setup times," Operational Research, Springer, vol. 18(1), pages 123-158, April.
    7. Lvjiang Yin & Xinyu Li & Chao Lu & Liang Gao, 2016. "Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm," Sustainability, MDPI, vol. 8(12), pages 1-33, December.
    8. Jia, Shun & Yuan, Qinghe & Lv, Jingxiang & Liu, Ying & Ren, Dawei & Zhang, Zhongwei, 2017. "Therblig-embedded value stream mapping method for lean energy machining," Energy, Elsevier, vol. 138(C), pages 1081-1098.
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    Cited by:

    1. Chen-Yang Cheng & Shih-Wei Lin & Pourya Pourhejazy & Kuo-Ching Ying & Yu-Zhe Lin, 2021. "No-Idle Flowshop Scheduling for Energy-Efficient Production: An Improved Optimization Framework," Mathematics, MDPI, vol. 9(12), pages 1-18, June.
    2. Konstantinos Salonitis, 2020. "Energy Efficiency of Manufacturing Processes and Systems—An Introduction," Energies, MDPI, vol. 13(11), pages 1-5, June.
    3. Nailiang Li & Caihong Feng, 2021. "Research on Machining Workshop Batch Scheduling Incorporating the Completion Time and Non-Processing Energy Consumption Considering Product Structure," Energies, MDPI, vol. 14(19), pages 1-26, September.
    4. Junfeng Wang & Zicheng Fei & Qing Chang & Shiqi Li, 2019. "Energy Saving Operation of Manufacturing System Based on Dynamic Adaptive Fuzzy Reasoning Petri Net," Energies, MDPI, vol. 12(11), pages 1-17, June.

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