Supply chain sustainability improvement using exergy analysis

• Published in: Computers & industrial engineering 2021-04, Vol.154, p.107142, Article 107142
• Main Authors: Naderi, Reihaneh, Shafiei Nikabadi, Mohsen, Alem Tabriz, Akbar, Pishvaee, Mir Saman
• Format: Article
• Language: English
• Subjects: Exergy analysis; Genetic algorithm; Simulated annealing; Supply chain; Sustainability
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2021.107142

•Developing a mathematical model to design a SSCM based on EEA and Entropy.•Utilizing metaheuristic algorithms for SSCM considering exergy analysis.•Presenting a hybrid global/local search algorithm based on GA and SA (named GLGASA)•The multi-objective problem is formulated based on economic costs and consumed exergy.•A real-world food supply chain is used to validate the proposed methodology. Sustainable supply chain management (SSCM) is an improved version of supply chain management, in which, not only economic issues but also social and environmental issues are considered. It can be achieved through extended exergy analysis which is a powerful thermodynamic tool for evaluating the sustainability of an industrial system. This paper provides an exergetic analysis to model and calculate the consumed exergy for sustainable supply chains. The model considers different objectives of financial, social and environmental aspects on selecting the more sustainable supply chain to produce and distribute productions. The proposed model is solved using a hybrid global- and local-search metaheuristic algorithm based on genetic algorithm and simulated annealing (named GLGASA). By minimizing the exergy cost, the authors provide an insight about the potential of the environmental destruction saving per unit of additional cost. This quantification of the cost and saving would be needed at the time of business case calculation for the new projects or modification and upgrade of the existing processes to achieve a better decision that guarantees both return of the investment and environmental protection. In order to validate the proposed methodology, a real food supply chain is presented and discussed to show the usability of the model and claim the benefits over the previously available models. According to the obtained results, the proposed method provides 4.48% saving in the consumed exergy of the supply chain by accepting additional economic costs.