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Horizontal integration management: An optimal switching model for parallel production system with multiple periods in smart supply chain environment

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  • Sun, Jing
  • Yamamoto, Hisashi
  • Matsui, Masayuki

Abstract

Horizontal integration via a new generation of global value chain network is a main characteristic of Industry 4.0. As a contribution to the achievement of smart supply chain, optimal operation management for horizontal integration of production network has been paid attention to recently. This paper aims to derive an optimal switch model considering production, due date and quality for parallel production system with multiple periods in smart supply environment. Due to the customer needs of cost and delivery date shorting, prompt change in the production plan became more important. In the multi period system (For instance, production line.) where target processing time exists, production, idle and delay risks occur repeatedly for multiple periods. In such situations, delay of one period may influence the delivery date of an entire process. This kind of problem is called “a limited-cycle problem with multiple periods”, and is seen in production lines, time-bucket balancing, and production seat systems and so on. In this paper, we discuss minimum expected cost including production, due date and quality in a parallel production process, where the risk depends on the previous situation and occurs repeatedly throughout multiple periods. Also, the policy of optimal switching for parallel production system will be analysed. The results of numeric experiment in this paper show that the proposed optimal switching model can contribute to the scientific knowledge on the development of integrated solutions for parallel production system.

Suggested Citation

  • Sun, Jing & Yamamoto, Hisashi & Matsui, Masayuki, 2020. "Horizontal integration management: An optimal switching model for parallel production system with multiple periods in smart supply chain environment," International Journal of Production Economics, Elsevier, vol. 221(C).
    • Handle: RePEc:eee:proeco:v:221:y:2020:i:c:s0925527319302853
    DOI: 10.1016/j.ijpe.2019.08.010
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    References listed on IDEAS

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    1. Fernanda Strozzi & Claudia Colicchia & Alessandro Creazza & Carlo Noè, 2017. "Literature review on the ‘Smart Factory’ concept using bibliometric tools," International Journal of Production Research, Taylor & Francis Journals, vol. 55(22), pages 6572-6591, November.
    2. Kyparisis, George J. & Koulamas, Christos, 2006. "Flexible flow shop scheduling with uniform parallel machines," European Journal of Operational Research, Elsevier, vol. 168(3), pages 985-997, February.
    3. S C H Leung & Y Wu & K K Lai, 2006. "A stochastic programming approach for multi-site aggregate production planning," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(2), pages 123-132, February.
    4. Wu, Xianyi & Zhou, Xian, 2008. "Stochastic scheduling to minimize expected maximum lateness," European Journal of Operational Research, Elsevier, vol. 190(1), pages 103-115, October.
    5. De Toni, Alberto & Meneghetti, Antonella, 2000. "The production planning process for a network of firms in the textile-apparel industry," International Journal of Production Economics, Elsevier, vol. 65(1), pages 17-32, April.
    6. Yongjian Li & Jihong Zhang & Jian Chen & Xiaoqiang Cai, 2010. "Optimal Solution Structure For Multi-Period Production Planning With Returned Products Remanufacturing," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 27(05), pages 629-648.
    7. Safaei, Nima & Tavakkoli-Moghaddam, Reza, 2009. "Integrated multi-period cell formation and subcontracting production planning in dynamic cellular manufacturing systems," International Journal of Production Economics, Elsevier, vol. 120(2), pages 301-314, August.
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    Cited by:

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    2. Katoozian, Hoora & Zanjani, Masoumeh Kazemi, 2022. "Supply network design for mass personalization in Industry 4.0 era," International Journal of Production Economics, Elsevier, vol. 244(C).
    3. Benitez, Guilherme Brittes & Ghezzi, Antonio & Frank, Alejandro G., 2023. "When technologies become Industry 4.0 platforms: Defining the role of digital technologies through a boundary-spanning perspective," International Journal of Production Economics, Elsevier, vol. 260(C).

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