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Economic Ordering Frequency for two Items Jointly Replenished

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  • Frank T. Shu

    (Mobil Sekiyu Kabushiki Kaisha, Tokyo)

Abstract

The decision model to be presented is typically applicable to the batch processing industry where a batch of product is blended and subsequently packaged into various types of containers. When a product is packaged into more than one type container immediately after its manufacture, these items are said to be jointly replenished. In the case of joint replenishment, the economic lot size for each item cannot be determined without considering the rest of the items within the group. This is so because each time a batch of a product is made, there is a primary set-up cost for the manufacture of the product and a secondary set-up cost for each of the items to be packaged within the jointly replenished group. If the packaging set-up costs are very high, it may be more economical to package certain "slow moving" items less often than the "fast moving items." Specifically, one could package a "slow moving" item once every other time or every third time a batch of product is made. An analytical model has been developed to provide a set of criteria which one can use as a basis for applying "skipping rules" to the slow moving item within a jointly replenished group.

Suggested Citation

  • Frank T. Shu, 1971. "Economic Ordering Frequency for two Items Jointly Replenished," Management Science, INFORMS, vol. 17(6), pages 406-410, February.
  • Handle: RePEc:inm:ormnsc:v:17:y:1971:i:6:p:b406-b410
    DOI: 10.1287/mnsc.17.6.B406
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    Cited by:

    1. Ata Allah Taleizadeh & Hadi Samimi & Babak Mohammadi, 2015. "Joint replenishment policy with backordering and special sale," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(7), pages 1172-1198, May.
    2. Saeed Poormoaied, 2022. "Inventory decision in a periodic review inventory model with two complementary products," Annals of Operations Research, Springer, vol. 315(2), pages 1937-1970, August.
    3. Tsai, Chi-Yang & Yeh, Szu-Wei, 2008. "A multiple objective particle swarm optimization approach for inventory classification," International Journal of Production Economics, Elsevier, vol. 114(2), pages 656-666, August.
    4. Tamar Cohen-Hillel & Liron Yedidsion, 2018. "The Periodic Joint Replenishment Problem Is Strongly 𝒩𝒫-Hard," Mathematics of Operations Research, INFORMS, vol. 43(4), pages 1269-1289, November.
    5. Khouja, Moutaz & Goyal, Suresh, 2008. "A review of the joint replenishment problem literature: 1989-2005," European Journal of Operational Research, Elsevier, vol. 186(1), pages 1-16, April.
    6. Nicholas G. Hall, 1989. "The inventory packing problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 36(4), pages 399-418, August.
    7. He-Yau Kang & Amy H.I. Lee & Chien-Wei Wu & Cheng-Han Lee, 2017. "An efficient method for dynamic-demand joint replenishment problem with multiple suppliers and multiple vehicles," International Journal of Production Research, Taylor & Francis Journals, vol. 55(4), pages 1065-1084, February.
    8. Yao, Ming-Jong & Lin, Jen-Yen & Lin, Yu-Liang & Fang, Shu-Cherng, 2020. "An integrated algorithm for solving multi-customer joint replenishment problem with districting consideration," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    9. Li, Jian & Wang, Shouyang & Cheng, T.C. Edwin, 2008. "Analysis of postponement strategy by EPQ-based models with planned backorders," Omega, Elsevier, vol. 36(5), pages 777-788, October.
    10. Ongkunaruk, P. & Wahab, M.I.M. & Chen, Y., 2016. "A genetic algorithm for a joint replenishment problem with resource and shipment constraints and defective items," International Journal of Production Economics, Elsevier, vol. 175(C), pages 142-152.
    11. Olsen, Anne L., 2008. "Inventory replenishment with interdependent ordering costs: An evolutionary algorithm solution," International Journal of Production Economics, Elsevier, vol. 113(1), pages 359-369, May.

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