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Green Supply Chain Network Optimization Under Random and Fuzzy Environment

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Abstract

This paper established a three-level supply chain composed of plants, distribution centers, and retailers, and studied the location of distribution centers in the supply chain network and the carbon emissions during processing and transportation. In a random and fuzzy environment, the research objective is to minimize the supply chain’s cost and carbon emission. The multi-objective uncertain equilibrium model of the green supply chain network is established by introducing opportunity constraints, and the stability of the model can be enhanced by using variance function and risk function. Then this research integrated the theory of stochastic programming and fuzzy mathematical programming and employed Monte Carlo simulation; the sample mean approximation, chance-constrained programming and fuzzy expectation to deal with the random parameters and fuzzy parameters in the model so that the uncertain model is clarified. Further, the authors used the hierarchical method, the weighted ideal point method, restriction method, and weighted ideal point method to solve the multi-objective model. Finally, a numerical example is provided to demonstrate the feasibility of the model.

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Acknowledgements

This work supported by the Beijing Key Laboratory of Megaregions Sustainable Development Modelling, Capital University of Economics and Business (No. MCR2019QN09) and China Postdoctoral Science Foundation (No. 2019M660700).

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Authors and Affiliations

  1. School of Economics and Management, Chang’an University, Xi’an, China

    Zhang Yu

  2. School of Economics and Management, Tsinghua University, Beijing, China

    Syed Abdul Rehman Khan

  3. School of Management, Xuzhou University of Technology, Xuzhou, China

    Syed Abdul Rehman Khan

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  1. Zhang Yu
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  2. Syed Abdul Rehman Khan
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Correspondence to Syed Abdul Rehman Khan.

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Yu, Z., Khan, S.A.R. Green Supply Chain Network Optimization Under Random and Fuzzy Environment. Int. J. Fuzzy Syst. 24, 1170–1181 (2022). https://doi.org/10.1007/s40815-020-00979-7

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  • DOI: https://doi.org/10.1007/s40815-020-00979-7

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