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Coordination of supply chains with a flexible ordering policy under yield and demand uncertainty

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  • Hu, Fei
  • Lim, Cheng-Chew
  • Lu, Zudi

Abstract

This paper studies a flexible ordering policy among a manufacturer and a supplier with random yield and demand uncertainty, where the order quantity lies between the minimum and the maximum quantity. We first determine the optimal flexible ordering policy and the corresponding raw material production quantity that maximize expected profit of the centralized supply chain, and find that our flexible ordering policy can significantly enhance the supply chain's expected profit compared to an invariable ordering policy. Then we analyze the decentralized scenario and propose a revenue sharing policy with an order penalty and rebate (OPR) contract to fully coordinate the supply chain. Finally, numerical examples are given to illustrate the results.

Suggested Citation

  • Hu, Fei & Lim, Cheng-Chew & Lu, Zudi, 2013. "Coordination of supply chains with a flexible ordering policy under yield and demand uncertainty," International Journal of Production Economics, Elsevier, vol. 146(2), pages 686-693.
    • Handle: RePEc:eee:proeco:v:146:y:2013:i:2:p:686-693
    DOI: 10.1016/j.ijpe.2013.08.024
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    4. B.C. Giri & S. Bardhan & T. Maiti, 2016. "Coordinating a three-layer supply chain with uncertain demand and random yield," International Journal of Production Research, Taylor & Francis Journals, vol. 54(8), pages 2499-2518, April.
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    9. Boyi Zhang & Shaojian Qu, 2023. "Green Supply Chain Game: Decision-Making and Coordination Mechanism Design," Sustainability, MDPI, vol. 15(21), pages 1-25, November.
    10. Senlin Zhao & Qinghua Zhu, 2017. "Remanufacturing supply chain coordination under the stochastic remanufacturability rate and the random demand," Annals of Operations Research, Springer, vol. 257(1), pages 661-695, October.
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    18. Ata Allah Taleizadeh, 2017. "Stochastic Multi-Objectives Supply Chain Optimization with Forecasting Partial Backordering Rate: A Novel Hybrid Method of Meta Goal Programming and Evolutionary Algorithms," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(04), pages 1-28, August.
    19. Baruah, Pundarikaksha & Chinnam, Ratna Babu & Korostelev, Alexander & Dalkiran, Evrim, 2016. "Optimal soft-order revisions under demand and supply uncertainty and upstream information," International Journal of Production Economics, Elsevier, vol. 182(C), pages 14-25.
    20. Rongfang Yan & Bin Lu & Jianjun Wu, 2016. "Contract Coordination Strategy of Supply Chain with Substitution under Supply Disruption and Stochastic Demand," Sustainability, MDPI, vol. 8(7), pages 1-17, July.
    21. Adhikari, Arnab & Bisi, Arnab & Avittathur, Balram, 2020. "Coordination mechanism, risk sharing, and risk aversion in a five-level textile supply chain under demand and supply uncertainty," European Journal of Operational Research, Elsevier, vol. 282(1), pages 93-107.
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    23. Hsieh, Chung-Chi & Chang, Yih-Long & Wu, Cheng-Han, 2014. "Competitive pricing and ordering decisions in a multiple-channel supply chain," International Journal of Production Economics, Elsevier, vol. 154(C), pages 156-165.
    24. Jiarong Luo & Xu Chen, 2017. "Risk hedging via option contracts in a random yield supply chain," Annals of Operations Research, Springer, vol. 257(1), pages 697-719, October.
    25. Hezarkhani, Behzad & Demirel, Guven & Bouchery, Yann & Dora, Manoj, 2023. "Can “ugly veg” supply chains reduce food loss?," European Journal of Operational Research, Elsevier, vol. 309(1), pages 117-132.

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