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Production planning for closed-loop supply chains with product recovery and reuse: an analytical approach

Jayaraman, Vaidyanathan·2006·International Journal of Production Research

Abstract

We present an analytical approach towards production planning and control for closed-loop supply chains with product recovery and reuse. This approach consists of a mathematical programming model, RAPP (Remanufacturing Aggregate Production Planning), for aggregate production planning and control. The model is designed to aid operational decision-makers in an intermediate to long-range planning environment and also may serve as a focal point for developing formal systems for production planning, inventory control, and other tactical decision-making. Key decisions include the number of units of core type with a nominal quality level that is disassembled, disposed, remanufactured and acquired in a given time period. Other decisions pertain to the inventory of modules and cores that remain at the end of a given time period. We use data from a company that remanufactures mobile telephones to validate our mathematical programming model. Managerial implications of such a closed-loop model as well as future research and conclusions are provided at the end.

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Metadata

Authors
Jayaraman, V.
Venue
International Journal of Production Research
Published
2006-03
Volume
44
Issue
5
Pages
981-998
ISSN
  • 0020-7543 (ISSN-L)
  • 1366-588X
OpenAlex
W2011287999
Cited by
241
References
0
FWCI
22.31
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Production planning for closed-loop supply chains with product recovery and reuse: an analytical approach. https://app.cochat.ai/papers/10.1080/00207540500250507
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Jayaraman, V. (2006). Production planning for closed-loop supply chains with product recovery and reuse: an analytical approach. International Journal of Production Research. https://doi.org/10.1080/00207540500250507

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.