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Robust supply chain networks design and ambiguous risk preferences

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  • Guodong Yu
  • Fei Li
  • Yu Yang

Abstract

In this paper, a robust stochastic optimisation model for regret minimising is proposed for supply chain networks design. The model emphasises the ambiguity lying in both the risk preference and the probability distribution. A duality theory for the model is derived and the random utility functions are identified as the Lagrange multipliers. In addition, a tractable relaxation based on reformulation-linearisation technique is presented for the computational aspects of the model. The numerical experiments show that our regret minimising model can make a well balance between the conservativeness of a pure robust optimisation model and the optimism of risk-neutrality. We also present a case study to demonstrate the applicability of the proposed model.

Suggested Citation

  • Guodong Yu & Fei Li & Yu Yang, 2017. "Robust supply chain networks design and ambiguous risk preferences," International Journal of Production Research, Taylor & Francis Journals, vol. 55(4), pages 1168-1182, February.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:4:p:1168-1182
    DOI: 10.1080/00207543.2016.1232499
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    Cited by:

    1. Lingyun Zhou & Dezhi Zhang & Shuangyan Li & Xiangyu Luo, 2023. "An Integrated Optimization Model of Green Supply Chain Network Design with Inventory Management," Sustainability, MDPI, vol. 15(16), pages 1-24, August.
    2. Xiqiang Xia & Junhu Ruan & Zhiru Juan & Yan Shi & Xuping Wang & Felix T. S. Chan, 2018. "Upstream-Downstream Joint Carbon Reduction Strategies Based on Low-Carbon Promotion," IJERPH, MDPI, vol. 15(7), pages 1-16, June.
    3. Saxena, Neha & Sarkar, Biswajit, 2023. "How does the retailing industry decide the best replenishment strategy by utilizing technological support through blockchain?," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    4. Tsao, Yu-Chung & Vu, Thuy-Linh, 2019. "Power supply chain network design problem for smart grid considering differential pricing and buy-back policies," Energy Economics, Elsevier, vol. 81(C), pages 493-502.
    5. Pawlicka Kinga & Bal Monika, 2022. "Sustainable Supply Chain Finances implementation model and Artificial Intelligence for innovative omnichannel logistics," Management, Sciendo, vol. 26(1), pages 19-35, January.
    6. Sharfuddin Ahmed Khan & Muhammad Shujaat Mubarik & Simonov Kusi‐Sarpong & Himanshu Gupta & Syed Imran Zaman & Mobashar Mubarik, 2022. "Blockchain technologies as enablers of supply chain mapping for sustainable supply chains," Business Strategy and the Environment, Wiley Blackwell, vol. 31(8), pages 3742-3756, December.
    7. Luttiely Santos Oliveira & Ricardo Luiz Machado, 2021. "Application of optimization methods in the closed-loop supply chain: a literature review," Journal of Combinatorial Optimization, Springer, vol. 41(2), pages 357-400, February.
    8. Xiao Zhao & Xuhui Xia & Guodong Yu, 2019. "Primal-Dual Learning Based Risk-Averse Optimal Integrated Allocation of Hybrid Energy Generation Plants under Uncertainty," Energies, MDPI, vol. 12(12), pages 1-15, June.

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