IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v290y2020i1d10.1007_s10479-018-2969-x.html
   My bibliography  Save this article

Multi-objective genetic algorithm for energy-efficient hybrid flow shop scheduling with lot streaming

Author

Listed:
  • Tzu-Li Chen

    (Fu Jen Catholic University)

  • Chen-Yang Cheng

    (National Taipei University of Technology)

  • Yi-Han Chou

    (Fu Jen Catholic University)

Abstract

Hybrid flow shop scheduling problems are encountered in many real-world manufacturing operations such as computer assembly, TFT-LCD module assembly, and solar cell manufacturing. Most research considers the scheduling problem in regard to time requirements and the steps needed to improve production efficiency. However, the increasing amount of carbon emissions worldwide is contributing to the worsening global warming problem. Many countries and international organizations have started to pay attention to this problem, even creating mechanisms to reduce carbon emissions. Furthermore, manufacturing enterprises are showing growing interest in realizing energy savings. Thus, the present research study focuses on reducing energy costs and completion time at the manufacturing-system level. This paper proposed a multi-objective mixed-integer programming for energy-efficient hybrid flow shop scheduling with lot streaming in order to minimize both the production makespan and electric power consumption. Due to a trade-off between these objectives and the computational complexity of the proposed multi-objective mixed-integer program, this study adopts the genetic algorithm (GA) to obtain approximate Pareto solutions more efficiently. In addition, a multi-objective energy efficiency scheduling algorithm is also developed to calculate the fitness values of each chromosome in GA.

Suggested Citation

  • Tzu-Li Chen & Chen-Yang Cheng & Yi-Han Chou, 2020. "Multi-objective genetic algorithm for energy-efficient hybrid flow shop scheduling with lot streaming," Annals of Operations Research, Springer, vol. 290(1), pages 813-836, July.
    • Handle: RePEc:spr:annopr:v:290:y:2020:i:1:d:10.1007_s10479-018-2969-x
    DOI: 10.1007/s10479-018-2969-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-018-2969-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-018-2969-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lee, Wen-Chiung & Chung, Yu-Hsiang, 2013. "Permutation flowshop scheduling to minimize the total tardiness with learning effects," International Journal of Production Economics, Elsevier, vol. 141(1), pages 327-334.
    2. 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.
    3. Martin, Clarence H, 2009. "A hybrid genetic algorithm/mathematical programming approach to the multi-family flowshop scheduling problem with lot streaming," Omega, Elsevier, vol. 37(1), pages 126-137, February.
    4. Tan, K.C. & Goh, C.K. & Yang, Y.J. & Lee, T.H., 2006. "Evolving better population distribution and exploration in evolutionary multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 171(2), pages 463-495, June.
    5. Leonard Adler & Nelson Fraiman & Edward Kobacker & Michael Pinedo & Juan Carlos Plotnicoff & Tso Pang Wu, 1993. "BPSS: A Scheduling Support System for the Packaging Industry," Operations Research, INFORMS, vol. 41(4), pages 641-648, August.
    6. Robert J. Wittrock, 1988. "An Adaptable Scheduling Algorithm for Flexible Flow Lines," Operations Research, INFORMS, vol. 36(3), pages 445-453, June.
    7. Zhang, Wei & Yin, Changyu & Liu, Jiyin & Linn, Richard J., 2005. "Multi-job lot streaming to minimize the mean completion time in m-1 hybrid flowshops," International Journal of Production Economics, Elsevier, vol. 96(2), pages 189-200, May.
    8. Liu, Jiyin, 2008. "Single-job lot streaming in m - 1 two-stage hybrid flowshops," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1171-1183, June.
    9. C N Potts & V A Strusevich, 2009. "Fifty years of scheduling: a survey of milestones," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 41-68, May.
    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. Sen, Alper & Topaloglu, Engin & Benli, Omer S., 1998. "Optimal streaming of a single job in a two-stage flow shop," European Journal of Operational Research, Elsevier, vol. 110(1), pages 42-62, October.
    12. Baker, KR & Jia, D, 1993. "A comparative study of lot streaming procedures," Omega, Elsevier, vol. 21(5), pages 561-566, September.
    13. Glass, C. A. & Gupta, J. N. D. & Potts, C. N., 1994. "Lot streaming in three-stage production processes," European Journal of Operational Research, Elsevier, vol. 75(2), pages 378-394, June.
    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. 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.
    2. 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.
    3. Zhe Liu & Shurong Li, 2022. "A numerical method for interval multi-objective mixed-integer optimal control problems based on quantum heuristic algorithm," Annals of Operations Research, Springer, vol. 311(2), pages 853-898, April.
    4. Markus Hilbert & Andreas Dellnitz & Andreas Kleine, 2023. "Production planning under RTP, TOU and PPA considering a redox flow battery storage system," Annals of Operations Research, Springer, vol. 328(2), pages 1409-1436, September.
    5. Wenjie Wang & Guangdong Tian & Gang Yuan & Duc Truong Pham, 2023. "Energy-time tradeoffs for remanufacturing system scheduling using an invasive weed optimization algorithm," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1065-1083, March.
    6. Ariel K. H. Lui & Maggie C. M. Lee & Eric W. T. Ngai, 2022. "Impact of artificial intelligence investment on firm value," Annals of Operations Research, Springer, vol. 308(1), pages 373-388, January.
    7. Zhe Zhang & Xiaoling Song & Huijun Huang & Yong Yin & Benjamin Lev, 2022. "Scheduling problem in seru production system considering DeJong’s learning effect and job splitting," Annals of Operations Research, Springer, vol. 312(2), pages 1119-1141, May.

    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 & Ruiz, Rubén, 2012. "An estimation of distribution algorithm for lot-streaming flow shop problems with setup times," Omega, Elsevier, vol. 40(2), pages 166-180, April.
    2. 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.
    3. D Biskup & M Feldmann, 2006. "Lot streaming with variable sublots: an integer programming formulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(3), pages 296-303, March.
    4. Zhang, Wei & Yin, Changyu & Liu, Jiyin & Linn, Richard J., 2005. "Multi-job lot streaming to minimize the mean completion time in m-1 hybrid flowshops," International Journal of Production Economics, Elsevier, vol. 96(2), pages 189-200, May.
    5. Chengshuai Li & Biao Zhang & Yuyan Han & Yuting Wang & Junqing Li & Kaizhou Gao, 2022. "Energy-Efficient Hybrid Flowshop Scheduling with Consistent Sublots Using an Improved Cooperative Coevolutionary Algorithm," Mathematics, MDPI, vol. 11(1), pages 1-27, December.
    6. Mansouri, S. Afshin & Aktas, Emel & Besikci, Umut, 2016. "Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption," European Journal of Operational Research, Elsevier, vol. 248(3), pages 772-788.
    7. An, Xiangxin & Si, Guojin & Xia, Tangbin & Wang, Dong & Pan, Ershun & Xi, Lifeng, 2023. "An energy-efficient collaborative strategy of maintenance planning and production scheduling for serial-parallel systems under time-of-use tariffs," Applied Energy, Elsevier, vol. 336(C).
    8. Mingxing Li & Ray Y. Zhong & Ting Qu & George Q. Huang, 2022. "Spatial–temporal out-of-order execution for advanced planning and scheduling in cyber-physical factories," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1355-1372, June.
    9. 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.
    10. 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.
    11. Gribkovskaia, Irina V. & Kovalev, Sergey & Werner, Frank, 2010. "Batching for work and rework processes on dedicated facilities to minimize the makespan," Omega, Elsevier, vol. 38(6), pages 522-527, December.
    12. 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.
    13. Adar A. Kalir & Subhash C. Sarin, 2003. "Constructing Near Optimal Schedules for the Flow-Shop Lot Streaming Problem with Sublot-Attached Setups," Journal of Combinatorial Optimization, Springer, vol. 7(1), pages 23-44, March.
    14. Liu, Jiyin, 2008. "Single-job lot streaming in m - 1 two-stage hybrid flowshops," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1171-1183, June.
    15. Liu, Ming & Yang, Xuenan & Chu, Feng & Zhang, Jiantong & Chu, Chengbin, 2020. "Energy-oriented bi-objective optimization for the tempered glass scheduling," Omega, Elsevier, vol. 90(C).
    16. 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.
    17. Zhi Li & Ray Y. Zhong & Ali Vatankhah Barenji & J. J. Liu & C. X. Yu & George Q. Huang, 2021. "Bi-objective hybrid flow shop scheduling with common due date," Operational Research, Springer, vol. 21(2), pages 1153-1178, June.
    18. Gheisariha, Elmira & Tavana, Madjid & Jolai, Fariborz & Rabiee, Meysam, 2021. "A simulation–optimization model for solving flexible flow shop scheduling problems with rework and transportation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 180(C), pages 152-178.
    19. 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.
    20. I. Van Nieuwenhuyse, 2008. "Lot sizing in stochastic flowshops. Striking the balance between capacity and flow," Review of Business and Economic Literature, KU Leuven, Faculty of Economics and Business (FEB), Review of Business and Economic Literature, vol. 0(2), pages 147-160.

    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:spr:annopr:v:290:y:2020:i:1:d:10.1007_s10479-018-2969-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.