IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v236y2014i2p774-786.html
   My bibliography  Save this article

Mathematical analysis and solutions for multi-objective line-cell conversion problem

Author

Listed:
  • Yu, Yang
  • Tang, Jiafu
  • Gong, Jun
  • Yin, Yong
  • Kaku, Ikou

Abstract

The line-cell (or line-seru) conversion is an innovation of assembly system applied widely in the electronics industry. Its essence is tearing out an assembly line and adopting a mini-assembly unit, called seru (or Japanese style assembly cell). In this paper, we develop a multi-objective optimization model to investigate two line-cell conversion performances: the total throughput time (TTPT) and the total labor hours (TLH). We analyze the bi-objective model to find out its mathematical characteristics such as solution space, combinatorial complexity and non-convex properties, and others. Owing to the difficulties of the model, a non-dominated sorting genetic algorithm that can solve large size problems in a reasonable time is developed. To verify the reliability of the algorithm, solutions are compared with those obtained from the enumeration method. We find that the proposed genetic algorithm is useful and can get reliable solutions in most cases.

Suggested Citation

  • Yu, Yang & Tang, Jiafu & Gong, Jun & Yin, Yong & Kaku, Ikou, 2014. "Mathematical analysis and solutions for multi-objective line-cell conversion problem," European Journal of Operational Research, Elsevier, vol. 236(2), pages 774-786.
    • Handle: RePEc:eee:ejores:v:236:y:2014:i:2:p:774-786
    DOI: 10.1016/j.ejor.2014.01.029
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221714000502
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2014.01.029?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. Yu, Yang & Tang, Jiafu & Sun, Wei & Yin, Yong & Kaku, Ikou, 2013. "Reducing worker(s) by converting assembly line into a pure cell system," International Journal of Production Economics, Elsevier, vol. 145(2), pages 799-806.
    2. Lin, Yi-Kuei & Yeh, Cheng-Ta, 2012. "Multi-objective optimization for stochastic computer networks using NSGA-II and TOPSIS," European Journal of Operational Research, Elsevier, vol. 218(3), pages 735-746.
    3. Arroyo, Jose Elias Claudio & Armentano, Vinicius Amaral, 2005. "Genetic local search for multi-objective flowshop scheduling problems," European Journal of Operational Research, Elsevier, vol. 167(3), pages 717-738, December.
    4. Coutinho-Rodrigues, João & Tralhão, Lino & Alçada-Almeida, Luís, 2012. "A bi-objective modeling approach applied to an urban semi-desirable facility location problem," European Journal of Operational Research, Elsevier, vol. 223(1), pages 203-213.
    5. Kathryn E. Stecke & Yong Yin & Ikou Kaku & Yasuhiko Murase, 2012. "Seru: The Organizational Extension of JIT for a Super-Talent Factory," International Journal of Strategic Decision Sciences (IJSDS), IGI Global, vol. 3(1), pages 106-119, January.
    6. Ali, Musrrat. & Siarry, Patrick & Pant, Millie., 2012. "An efficient Differential Evolution based algorithm for solving multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 217(2), pages 404-416.
    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. Ye Wang & Jiafu Tang, 2022. "Optimized skill configuration for the seru production system under an uncertain demand," Annals of Operations Research, Springer, vol. 316(1), pages 445-465, September.
    2. Kuo-Ching Ying & Yi-Ju Tsai, 2017. "Minimising total cost for training and assigning multiskilled workers in production systems," International Journal of Production Research, Taylor & Francis Journals, vol. 55(10), pages 2978-2989, May.
    3. Zhang, Zhe & Gong, Xue & Song, Xiaoling & Yin, Yong & Lev, Benjamin & Chen, Jie, 2022. "A column generation-based exact solution method for seru scheduling problems," Omega, Elsevier, vol. 108(C).
    4. Zhang, Zhe & Song, Xiaoling & Huang, Huijung & Zhou, Xiaoyang & Yin, Yong, 2022. "Logic-based Benders decomposition method for the seru scheduling problem with sequence-dependent setup time and DeJong’s learning effect," European Journal of Operational Research, Elsevier, vol. 297(3), pages 866-877.
    5. Li, Dongni & Jiang, Yuzhou & Zhang, Jinhui & Cui, Zihua & Yin, Yong, 2023. "An on-line seru scheduling algorithm with proactive waiting considering resource conflicts," European Journal of Operational Research, Elsevier, vol. 309(2), pages 506-515.
    6. 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. Ye Wang & Jiafu Tang, 2022. "Optimized skill configuration for the seru production system under an uncertain demand," Annals of Operations Research, Springer, vol. 316(1), pages 445-465, September.
    2. Li, Dongni & Lyu, Yao & Zhang, Jinhui & Cui, Zihua & Yin, Yong, 2024. "Order sequencing for a bucket brigade seru in a mass customization environment," International Journal of Production Economics, Elsevier, vol. 270(C).
    3. 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.
    4. Zhang, Zhe & Gong, Xue & Song, Xiaoling & Yin, Yong & Lev, Benjamin & Chen, Jie, 2022. "A column generation-based exact solution method for seru scheduling problems," Omega, Elsevier, vol. 108(C).
    5. Zhang, Zhe & Song, Xiaoling & Huang, Huijung & Zhou, Xiaoyang & Yin, Yong, 2022. "Logic-based Benders decomposition method for the seru scheduling problem with sequence-dependent setup time and DeJong’s learning effect," European Journal of Operational Research, Elsevier, vol. 297(3), pages 866-877.
    6. Kuo-Ching Ying & Yi-Ju Tsai, 2017. "Minimising total cost for training and assigning multiskilled workers in production systems," International Journal of Production Research, Taylor & Francis Journals, vol. 55(10), pages 2978-2989, May.
    7. Mahalec, Vladimir & Chen, Yingwu & Liu, Xiaolu & He, Renjie & Sun, Kai, 2015. "Reconfiguration of satellite orbit for cooperative observation using variable-size multi-objective differential evolutionAuthor-Name: Chen, Yingguo," European Journal of Operational Research, Elsevier, vol. 242(1), pages 10-20.
    8. Li, Dongni & Jiang, Yuzhou & Zhang, Jinhui & Cui, Zihua & Yin, Yong, 2023. "An on-line seru scheduling algorithm with proactive waiting considering resource conflicts," European Journal of Operational Research, Elsevier, vol. 309(2), pages 506-515.
    9. Xu, Jiuping & Song, Xiaoling & Wu, Yimin & Zeng, Ziqiang, 2015. "GIS-modelling based coal-fired power plant site identification and selection," Applied Energy, Elsevier, vol. 159(C), pages 520-539.
    10. Máximo Méndez & Mariano Frutos & Fabio Miguel & Ricardo Aguasca-Colomo, 2020. "TOPSIS Decision on Approximate Pareto Fronts by Using Evolutionary Algorithms: Application to an Engineering Design Problem," Mathematics, MDPI, vol. 8(11), pages 1-27, November.
    11. Erenay, Fatih Safa & Sabuncuoglu, Ihsan & Toptal, Aysegül & Tiwari, Manoj Kumar, 2010. "New solution methods for single machine bicriteria scheduling problem: Minimization of average flowtime and number of tardy jobs," European Journal of Operational Research, Elsevier, vol. 201(1), pages 89-98, February.
    12. Hua, Hao & Hovestadt, Ludger & Tang, Peng & Li, Biao, 2019. "Integer programming for urban design," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1125-1137.
    13. Om Prakash Verma & Toufiq Haji Mohammed & Shubham Mangal & Gaurav Manik, 2018. "Optimization of steam economy and consumption of heptad’s effect evaporator system in Kraft recovery process," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(1), pages 111-130, February.
    14. Sun, Yige & Chung, Sai-Ho & Wen, Xin & Ma, Hoi-Lam, 2021. "Novel robotic job-shop scheduling models with deadlock and robot movement considerations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    15. Misagh Rahbari & Alireza Arshadi Khamseh & Yaser Sadati-Keneti & Mohammad Javad Jafari, 2022. "A risk-based green location-inventory-routing problem for hazardous materials: NSGA II, MOSA, and multi-objective black widow optimization," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(2), pages 2804-2840, February.
    16. Qinqin Fan & Xuefeng Yan, 2018. "Multi-objective modified differential evolution algorithm with archive-base mutation for solving multi-objective $$p$$ p -xylene oxidation process," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 35-49, January.
    17. Hammad, Ahmed W A & Akbarnezhad, Ali & Rey, David, 2017. "Sustainable urban facility location: Minimising noise pollution and network congestion," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 107(C), pages 38-59.
    18. Yu, Shiwei & Zheng, Shuhong & Gao, Shiwei & Yang, Juan, 2017. "A multi-objective decision model for investment in energy savings and emission reductions in coal mining," European Journal of Operational Research, Elsevier, vol. 260(1), pages 335-347.
    19. Chun-lin Xin & Shuo Liang & Feng-wu Shen, 2022. "Reconfiguration of garbage collection system based on Voronoi graph theory: a simulation case of Beijing region," Journal of Combinatorial Optimization, Springer, vol. 43(5), pages 953-973, July.
    20. Chang Liu & Zhen Li & Jiafu Tang & Xuequn Wang & Ming-Jong Yao, 2022. "How SERU production system improves manufacturing flexibility and firm performance: an empirical study in China," Annals of Operations Research, Springer, vol. 316(1), pages 529-554, September.

    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:eee:ejores:v:236:y:2014:i:2:p:774-786. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.