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This article investigates the use of closed-loop supply chains (CLSC) to im¬prove the sustainability and performance of automatic air conditioning systems. With the growing use of automated air conditioning systems in automobiles, it is necessary to analyze and optimize their efficiency even after their life span. To reliably anticipate the performance of automatic air conditioning systems, the suggested method employs a unique soft computing technology based on support vector machines (SVM). Furthermore, the research focuses on the deployment of CLSC, which allows for optimal product recovery and resource utilization. To optimize the multi-product, multi-time, multi-echelon network, a generalized CLSC model is built, considering costs, product recovery possibilities, unknown parameters, and environmental performance. The study sheds light on reverse logistics decision-making, such as centre placement and allocation, as well as cultivation of supplier relationships. Overall, this study’s incorporation of CLSC and the SVM-based performance prediction approach supports sustainable manufacturing practices. It emphasizes the significance of resource efficiency, waste minimization, and environmental effect mitigation in air conditioning system design, manufacture, and operation. Manufacturers may increase not just their environmental sustainability but also the performance and durability of their air conditioning systems by optimizing product recovery and utilizing CLSC. This study provides industry stakeholders with practical information, supporting the adoption of sustainable practices and contributing to a more sustainable and efficient manufacturing ecosystem.
PAPER REVISED: 2023-07-28
PAPER ACCEPTED: 2023-08-31
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THERMAL SCIENCE YEAR 2023, VOLUME 27, ISSUE Issue 6, PAGES [4789 - 4799]
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© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence