IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v8y2016i8p762-d75987.html
   My bibliography  Save this article

Estimation of Distribution Algorithm for Energy-Efficient Scheduling in Turning Processes

Author

Listed:
  • Fang Wang

    (State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
    School of Management, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Yunqing Rao

    (State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Chaoyong Zhang

    (State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Qiuhua Tang

    (School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Liping Zhang

    (School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China)

Abstract

With the increasing concern for the environment, energy-efficient scheduling of the manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer nonlinear programming model for the energy-efficient scheduling of the flexible flow shop. Accordingly, a new decoding method is developed for the optimization of both spindle speed and scheduling scheme simultaneously, and an estimation of the distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistics from a simplified practical case. Validation results of the proposed method show that the makespan is shortened to a large extent, and the consumed energy is significantly saved. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.

Suggested Citation

  • Fang Wang & Yunqing Rao & Chaoyong Zhang & Qiuhua Tang & Liping Zhang, 2016. "Estimation of Distribution Algorithm for Energy-Efficient Scheduling in Turning Processes," Sustainability, MDPI, vol. 8(8), pages 1-20, August.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:8:p:762-:d:75987
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/8/8/762/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/8/8/762/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ruiz, Rubén & Vázquez-Rodríguez, José Antonio, 2010. "The hybrid flow shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 205(1), pages 1-18, August.
    2. Zhou, Kaile & Yang, Shanlin, 2016. "Understanding household energy consumption behavior: The contribution of energy big data analytics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 810-819.
    3. Nawaz, Muhammad & Enscore Jr, E Emory & Ham, Inyong, 1983. "A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem," Omega, Elsevier, vol. 11(1), pages 91-95.
    4. Hyun Woo Jeon & Marco Taisch & Vittaldas V. Prabhu, 2015. "Modelling and analysis of energy footprint of manufacturing systems," International Journal of Production Research, Taylor & Francis Journals, vol. 53(23), pages 7049-7059, December.
    5. Herbert G. Campbell & Richard A. Dudek & Milton L. Smith, 1970. "A Heuristic Algorithm for the n Job, m Machine Sequencing Problem," Management Science, INFORMS, vol. 16(10), pages 630-637, June.
    6. Zhou, Nan & Fridley, David & McNeil, Michael & Zheng, Nina & Letschert, Virginie & Ke, Jing & Saheb, Yamina, 2011. "Analysis of potential energy saving and CO2 emission reduction of home appliances and commercial equipments in China," Energy Policy, Elsevier, vol. 39(8), pages 4541-4550, August.
    7. Kis, Tamas & Pesch, Erwin, 2005. "A review of exact solution methods for the non-preemptive multiprocessor flowshop problem," European Journal of Operational Research, Elsevier, vol. 164(3), pages 592-608, August.
    8. Vittaldas V. Prabhu & Damien Trentesaux & Marco Taisch, 2015. "Energy-aware manufacturing operations," International Journal of Production Research, Taylor & Francis Journals, vol. 53(23), pages 6994-7004, December.
    9. Ding, Jian-Ya & Song, Shiji & Wu, Cheng, 2016. "Carbon-efficient scheduling of flow shops by multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 248(3), pages 758-771.
    10. Luo, Hao & Du, Bing & Huang, George Q. & Chen, Huaping & Li, Xiaolin, 2013. "Hybrid flow shop scheduling considering machine electricity consumption cost," International Journal of Production Economics, Elsevier, vol. 146(2), pages 423-439.
    11. Azizoglu, Meral & Cakmak, Ergin & Kondakci, Suna, 2001. "A flexible flowshop problem with total flow time minimization," European Journal of Operational Research, Elsevier, vol. 132(3), pages 528-538, August.
    12. Kan Fang & Nelson Uhan & Fu Zhao & John Sutherland, 2013. "Flow shop scheduling with peak power consumption constraints," Annals of Operations Research, Springer, vol. 206(1), pages 115-145, July.
    13. Pan, Quan-Ke & Ruiz, Rubén, 2014. "An effective iterated greedy algorithm for the mixed no-idle permutation flowshop scheduling problem," Omega, Elsevier, vol. 44(C), pages 41-50.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hajo Terbrack & Thorsten Claus & Frank Herrmann, 2021. "Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme," Sustainability, MDPI, vol. 13(23), pages 1-32, December.
    2. Jin Huang & Liangliang Jin & Chaoyong Zhang, 2017. "Mathematical Modeling and a Hybrid NSGA-II Algorithm for Process Planning Problem Considering Machining Cost and Carbon Emission," Sustainability, MDPI, vol. 9(10), pages 1-18, September.
    3. Mei Li & Gai-Ge Wang & Helong Yu, 2021. "Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem," Mathematics, MDPI, vol. 9(18), pages 1-30, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pan, Quan-Ke & Gao, Liang & Li, Xin-Yu & Gao, Kai-Zhou, 2017. "Effective metaheuristics for scheduling a hybrid flowshop with sequence-dependent setup times," Applied Mathematics and Computation, Elsevier, vol. 303(C), pages 89-112.
    2. Perez-Gonzalez, Paz & Framinan, Jose M., 2024. "A review and classification on distributed permutation flowshop scheduling problems," European Journal of Operational Research, Elsevier, vol. 312(1), pages 1-21.
    3. Quadt, Daniel & Kuhn, Heinrich, 2007. "A taxonomy of flexible flow line scheduling procedures," European Journal of Operational Research, Elsevier, vol. 178(3), pages 686-698, May.
    4. Alvarez-Meaza, Izaskun & Zarrabeitia-Bilbao, Enara & Rio-Belver, Rosa-María & Garechana-Anacabe, Gaizka, 2021. "Green scheduling to achieve green manufacturing: Pursuing a research agenda by mapping science," Technology in Society, Elsevier, vol. 67(C).
    5. Yong Wang & Yuting Wang & Yuyan Han, 2023. "A Variant Iterated Greedy Algorithm Integrating Multiple Decoding Rules for Hybrid Blocking Flow Shop Scheduling Problem," Mathematics, MDPI, vol. 11(11), pages 1-25, May.
    6. Neufeld, Janis S. & Schulz, Sven & Buscher, Udo, 2023. "A systematic review of multi-objective hybrid flow shop scheduling," European Journal of Operational Research, Elsevier, vol. 309(1), pages 1-23.
    7. Ding, Jian-Ya & Song, Shiji & Wu, Cheng, 2016. "Carbon-efficient scheduling of flow shops by multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 248(3), pages 758-771.
    8. Zhou, Shengchao & Jin, Mingzhou & Du, Ni, 2020. "Energy-efficient scheduling of a single batch processing machine with dynamic job arrival times," Energy, Elsevier, vol. 209(C).
    9. Rossit, Daniel Alejandro & Tohmé, Fernando & Frutos, Mariano, 2018. "The Non-Permutation Flow-Shop scheduling problem: A literature review," Omega, Elsevier, vol. 77(C), pages 143-153.
    10. Deming Lei & Youlian Zheng & Xiuping Guo, 2017. "A shuffled frog-leaping algorithm for flexible job shop scheduling with the consideration of energy consumption," International Journal of Production Research, Taylor & Francis Journals, vol. 55(11), pages 3126-3140, June.
    11. Sündüz Dağ, 2013. "An Application On Flowshop Scheduling," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 1(1), pages 47-56, December.
    12. Solimanpur, M. & Vrat, Prem & Shankar, Ravi, 2004. "A heuristic to minimize makespan of cell scheduling problem," International Journal of Production Economics, Elsevier, vol. 88(3), pages 231-241, April.
    13. Hajo Terbrack & Thorsten Claus & Frank Herrmann, 2021. "Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme," Sustainability, MDPI, vol. 13(23), pages 1-32, December.
    14. Gupta, Jatinder N.D. & Koulamas, Christos & Kyparisis, George J., 2006. "Performance guarantees for flowshop heuristics to minimize makespan," European Journal of Operational Research, Elsevier, vol. 169(3), pages 865-872, March.
    15. Weiwei Cui & Biao Lu, 2020. "A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint," Sustainability, MDPI, vol. 12(10), pages 1-22, May.
    16. Fei Luan & Zongyan Cai & Shuqiang Wu & Shi Qiang Liu & Yixin He, 2019. "Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem with Discrete Whale Optimization Algorithm," Mathematics, MDPI, vol. 7(8), pages 1-17, August.
    17. Golpîra, Hêriş, 2020. "Smart Energy-Aware Manufacturing Plant Scheduling under Uncertainty: A Risk-Based Multi-Objective Robust Optimization Approach," Energy, Elsevier, vol. 209(C).
    18. Matin, Hossein N.Z. & Salmasi, Nasser & Shahvari, Omid, 2017. "Makespan minimization in flowshop batch processing problem with different batch compositions on machines," International Journal of Production Economics, Elsevier, vol. 193(C), pages 832-844.
    19. Ben-Daya, M. & Al-Fawzan, M., 1998. "A tabu search approach for the flow shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 109(1), pages 88-95, August.
    20. Guinet, Alain & Legrand, Marie, 1998. "Reduction of job-shop problems to flow-shop problems with precedence constraints," European Journal of Operational Research, Elsevier, vol. 109(1), pages 96-110, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:8:y:2016:i:8:p:762-:d:75987. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.