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Optimisation Models of Remanufacturing Uncertainties in Closed-Loop Supply Chains-A Review

The consideration of uncertainty in closed-loop supply chains (CLSC) is a crucial aspect that has been discussed frequently in recent research. Despite a wealth of literature on CLSC modelling, a recent comprehensive review of remanufacturing models in the CLSC incorporating an analysis of uncertain...

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Published in:IEEE access 2021, Vol.9, p.160533-160551
Main Authors: Ropi, Nuramilawahida Mat, Hishamuddin, Hawa, Wahab, Dzuraidah Abd, Saibani, Nizaroyani
Format: Article
Language:English
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Summary:The consideration of uncertainty in closed-loop supply chains (CLSC) is a crucial aspect that has been discussed frequently in recent research. Despite a wealth of literature on CLSC modelling, a recent comprehensive review of remanufacturing models in the CLSC incorporating an analysis of uncertainty trends has yet to be undertaken. The primary purpose of this paper is to analyse and review the existing research streams encompassing mathematical modelling and optimisation of remanufacturing systems in the CLSC, specifically in the context of decision-making methods. This paper focuses on reviewing remanufacturing uncertainties, such as demand, inventory, cost, return yield, as well as the environment and seeks to clarify solutions to problems, including production planning, network design, channel coordination, contract, pricing, supplier selection, and inventory management for the past eight years. A total of 118 research articles were analysed to discover trends on problems, uncertainty categories, mathematical modelling techniques, and solution approaches, as well as providing research gaps and potential future extensions for researchers of common interest. Results from the analysis show that network design and production planning are the most frequent problems considered by previous authors, while analysis of demand and return yield uncertainties in the remanufacturing cycle are the most preferred studies. Finally, limitations and future research needs for decision-support methods in the CLSC were identified such that risk domains can be further explored.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3132096