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Reducing worker(s) by converting assembly line into a pure cell system

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  • Yu, Yang
  • Tang, Jiafu
  • Sun, Wei
  • Yin, Yong
  • Kaku, Ikou

Abstract

The line-cell conversion is established as a new production system towards converting traditional conveyor assembly line to a cell system, in which one (or multiple) worker carries out all of the operations of a job in a cell. Its performance improvement can be enhanced by reducing worker(s) without decreasing productivity. How to conduct this conversion by determining how many cells should be formatted and which workers are assigned in a cell, is a complicated decision problem. This paper presents a multi-objective line-cell conversion model with the two goals of reducing worker(s) and increasing productivity simultaneously, in a production environment that converts traditional conveyor assembly line into a pure cell system. We identify several mathematical insights on solution space of the multi-objective line-cell conversion model and prove that it is an NP-hard problem. Then we provide an improved exact algorithm to obtain Pareto-optimal solutions of the multi-objective model. Several numerical simulation experiments are performed to illustrate that the line-cell conversion can be used to reduce worker(s) and the total throughput time at the same time.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:proeco:v:145:y:2013:i:2:p:799-806
    DOI: 10.1016/j.ijpe.2013.06.009
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    References listed on IDEAS

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    1. Chen, Yin-Yann & Cheng, Chen-Yang & Wang, Li-Chih & Chen, Tzu-Li, 2013. "A hybrid approach based on the variable neighborhood search and particle swarm optimization for parallel machine scheduling problems—A case study for solar cell industry," International Journal of Production Economics, Elsevier, vol. 141(1), pages 66-78.
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    4. Che, Ada & Chabrol, Michelle & Gourgand, Michel & Wang, Yuan, 2012. "Scheduling multiple robots in a no-wait re-entrant robotic flowshop," International Journal of Production Economics, Elsevier, vol. 135(1), pages 199-208.
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    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. 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.
    4. Battaïa, Olga & Delorme, Xavier & Dolgui, Alexandre & Hagemann, Johannes & Horlemann, Anika & Kovalev, Sergey & Malyutin, Sergey, 2015. "Workforce minimization for a mixed-model assembly line in the automotive industry," International Journal of Production Economics, Elsevier, vol. 170(PB), pages 489-500.
    5. 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).
    6. 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.
    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.

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