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A closed-loop supply chain with stochastic product returns and worker experience under learning and forgetting

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  • B.C. Giri
  • Christoph H. Glock

Abstract

This paper addresses a single-manufacturer single-retailer closed-loop supply chain with stochastic product returns considering worker experience under learning and forgetting in production and inspection of returned items at the manufacturer. Customer demand is assumed to be dependent linearly on the retail price, and it is fulfilled by using both manufactured and remanufactured products. The manufacturer delivers the buyer’s order quantity in a number of equal-sized batches. The optimal number of shipments, the shipment size and the retail price are determined by maximising the average expected profit of the closed-loop supply chain. It is observed from the numerical study that high learning effects in production and inspection lead to high recovery rates of used products, which, besides an economic advantage, may have a positive effect on the environment. Even though forgetting has an adverse effect, the average expected profit of the closed-loop supply chain is much higher than that of the basic model which ignores worker learning.

Suggested Citation

  • B.C. Giri & Christoph H. Glock, 2017. "A closed-loop supply chain with stochastic product returns and worker experience under learning and forgetting," International Journal of Production Research, Taylor & Francis Journals, vol. 55(22), pages 6760-6778, November.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:22:p:6760-6778
    DOI: 10.1080/00207543.2017.1347301
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    Citations

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    Cited by:

    1. B. C. Giri & M. Masanta, 2022. "A closed-loop supply chain model with uncertain return and learning-forgetting effect in production under consignment stock policy," Operational Research, Springer, vol. 22(2), pages 947-975, April.
    2. Gunasekara, Lahiru & Robb, David J. & Zhang, Abraham, 2023. "Used product acquisition, sorting and disposition for circular supply chains: Literature review and research directions," International Journal of Production Economics, Elsevier, vol. 260(C).
    3. M. Masanta & B. C. Giri, 2022. "A manufacturing–remanufacturing supply chain model with learning and forgetting in inspection under consignment stock agreement," Operational Research, Springer, vol. 22(4), pages 4093-4117, September.
    4. Lingli Shu & Xuedong Liang & Jingyuan Yao & Peng Luo, 2023. "Price Optimization of Closed-loop Supply Chain With Uncertain Demand Under Multi-channel Cross Influence," SAGE Open, , vol. 13(4), pages 21582440231, December.
    5. Chen, Daqiang & Ignatius, Joshua & Sun, Danzhi & Zhan, Shalei & Zhou, Chenyu & Marra, Marianna & Demirbag, Mehmet, 2019. "Reverse logistics pricing strategy for a green supply chain: A view of customers' environmental awareness," International Journal of Production Economics, Elsevier, vol. 217(C), pages 197-210.
    6. M. Masanta & B. C. Giri, 2022. "A closed-loop supply chain model with learning effect, random return and imperfect inspection under price- and quality-dependent demand," OPSEARCH, Springer;Operational Research Society of India, vol. 59(3), pages 1094-1115, September.
    7. Chen, Zhixiang & Bidanda, Bopaya, 2019. "Sustainable manufacturing production-inventory decision of multiple factories with JIT logistics, component recovery and emission control," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 356-383.

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