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Multi-objective job-shop scheduling with lot-splitting production

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  • Huang, Rong-Hwa

Abstract

While focusing on lot splitting in the job-shop scheduling problem, this study attempts to minimize the weighted total of stock, machine idle and carrying costs. Stock cost is determined using processing time. Machine idle cost is estimated using machine idle time. Carrying cost is calculated using the carry number of lot splitting. Results of this study demonstrate that stock cost and machine idle cost are inversely related to the number of lots split and have marginal decreasing result of benefit. The benefit of processing time is not as apparent as that of count and increase in turn. Carrying cost is positively related to the number of lots split. The minimum weighted total cost of stock, machine idle and carrying costs typically appears when the number of lots split is 2 or 3. The ant colony optimization (ACO) algorithm is used to solve the job-shop scheduling problem. Compared with the solution obtained by LINGO, the ACO algorithm performs well in scheduling and uses less time to solve the problem.

Suggested Citation

  • Huang, Rong-Hwa, 2010. "Multi-objective job-shop scheduling with lot-splitting production," International Journal of Production Economics, Elsevier, vol. 124(1), pages 206-213, March.
    • Handle: RePEc:eee:proeco:v:124:y:2010:i:1:p:206-213
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Rui & Song, Shiji & Wu, Cheng, 2013. "A hybrid artificial bee colony algorithm for the job shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 141(1), pages 167-178.
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    3. Rohaninejad, Mohammad & Hanzálek, Zdeněk, 2023. "Multi-level lot-sizing and job shop scheduling with lot-streaming: Reformulation and solution approaches," International Journal of Production Economics, Elsevier, vol. 263(C).
    4. Zhen Wang & Qianwang Deng & Like Zhang & Xiaoyan Liu, 2023. "Integrated scheduling of production, inventory and imperfect maintenance based on mutual feedback of supplier and demander in distributed environment," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3445-3467, December.
    5. James C. Chen & Tzu-Li Chen & Bayu Rezki Pratama & Qian-Fang Tu, 2018. "Capacity planning with ant colony optimization for TFT-LCD array manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1695-1713, December.
    6. W. Qin & J. Zhang & D. Song, 2018. "An improved ant colony algorithm for dynamic hybrid flow shop scheduling with uncertain processing time," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 891-904, April.
    7. Viren Parwani & Guiping Hu, 2021. "Improving Manufacturing Supply Chain by Integrating SMED and Production Scheduling," Logistics, MDPI, vol. 5(1), pages 1-14, January.
    8. 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.
    9. Vinod, V. & Sridharan, R., 2011. "Simulation modeling and analysis of due-date assignment methods and scheduling decision rules in a dynamic job shop production system," International Journal of Production Economics, Elsevier, vol. 129(1), pages 127-146, January.

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