Computers & Industrial Engineering | 2021

Optimal production decisions for remanufacturing end-of-life products under quality uncertainty and a carbon cap-and-trade policy

 
 
 
 
 

Abstract


Abstract Currently, the majority of available studies on remanufacturing mainly consider a single category of end-of-life (EOL) products under the uncertainty quality and carbon emissions policies, independently. However, enterprises have to refurbish multiple EOL products, consider the impact of uncertain quality on carbon emissions, and obey carbon emissions policies in real production. Therefore, this work considers both single and multiple categories of EOL products under carbon emissions uncertainty and a carbon cap-and-trade policy, and proposes three production decision models: (1) a traditional single-product remanufacturing model under fixed carbon emissions (FCE); (2) an extended single-product remanufacturing (ESPR) model under variable carbon emissions (VCE); and (3) a multi-product remanufacturing model. For these models, three strategies are developed to obtain the optimum profit, derive the minimum cost, and evaluate the effectiveness of the ESPR model. To validate the effects of the parameters on production decisions, numerical simulations of the VCE and FCE cases are applied in single- and multi-product settings. Besides, comparative studies with previous publications and real-life data analysis are carried out to illustrate the robustness and practicability of the models and strategies. The optimal decisions demonstrate that the proposed models considering the uncertainty of the quality and carbon emissions can obtain greater profit and lower carbon emissions, and these models for remanufacturing systems can be effectively applied under conditions of continuous quality and demand distributions. The remanufacturers and decision-makers can apply our models and strategies to choose better quality EOL products to be remanufactured, which can lead to better both economic and environmental benefits.

Volume None
Pages None
DOI 10.1016/j.cie.2021.107646
Language English
Journal Computers & Industrial Engineering

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