The research on optimization of auto supply chain network robust model under macroeconomic fluctuations

Chunxiang Guo, Xiaoli Liu, Maozhu Jin and Zhihan Lv

Chaos, Solitons & Fractals, 2016, vol. 89, issue C, 105-114

Abstract: Considering the uncertainty of the macroeconomic environment, the robust optimization method is studied for constructing and designing the automotive supply chain network, and based on the definition of robust solution a robust optimization model is built for integrated supply chain network design that consists of supplier selection problem and facility location–distribution problem. The tabu search algorithm is proposed for supply chain node configuration, analyzing the influence of the level of uncertainty on robust results, and by comparing the performance of supply chain network design through the stochastic programming model and robustness optimize model, on this basis, determining the rational layout of supply chain network under macroeconomic fluctuations. At last the contrastive test result validates that the performance of tabu search algorithm is outstanding on convergence and computational time. Meanwhile it is indicated that the robust optimization model can reduce investment risks effectively when it is applied to supply chain network design.

Keywords: Macroeconomic; Supply chain network design; Robust optimization; Auto industry; Competitive strategies (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:89:y:2016:i:c:p:105-114

DOI: 10.1016/j.chaos.2015.10.008

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