Green supply chain design: A mathematical modeling approach based on a multi-objective optimization model

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• 作者：Kartina Puji Nurjanni^a ; ^b ; Maria S. Carvalho^c ; Lino Costa^c ; ^{lac@dps.uminho.pt}
• 关键词：Green supply chain ; Closed loop network ; Multi-objective optimization ; Weighted sum method ; Tchebycheff approach
• 刊名：International Journal of Production Economics
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：183
• 期：part_PB
• 页码：421-432
• 全文大小：1504 K
• 卷排序：183

文摘

Increasing levels of industrialization of developed nations associated with globalization trends have been creating new challenges to supply chain management (SCM). For decades, the main focus of SCM has been on efficient ways of managing the flows through complex networks of supplier, producers and customers. More recently, and as a result of the exponential increase of energy and materials consumption rates of energy and materials, sustainable development arise as an urgent issue and new approaches to SCM are required to incorporate environmental and economic concerns in the design of supply chains. In this paper, a new green supply chain (GSC) design approach has been proposed to deal with the trade-offs between environmental and financial issues in order to reduce negative impacts on the environment caused by the increasing levels of industrialization. The new approach incorporates a closed loop network to accommodate the reprocessing paradigm of disposal products and a multi-objective optimization mathematical model to minimize overall costs and carbon dioxide emissions when setting the supply chain. Optimization process is performed using three scalarization approaches, namely weighted sum method, weighted Tchebycheff and augmented weighted Tchebycheff. Computational results are analyzed to identify the advantages and drawbacks of each approach. The model was tested in a case study and results allowed to identify the capability of the model to deal with the trade-offs between the costs and environmental issues as well as to identify its main limitation when addressing real size problems.