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Robust Supply Chain Network Equilibrium Model

Author

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  • Tatsuya Hirano

    (FUJITSU FIP Corporation, Tokyo, 105-8668 Japan)

  • Yasushi Narushima

    (Faculty of International Social Sciences, Yokohama National University, Hodogaya-ku, Yokohama, 240-8501 Japan)

Abstract

An important and often researched area of management science is mathematical modeling of a supply chain. Competitive situations can occur in supply chains owing to the involvement of multiple decision makers (players) that independently decide their behaviors. To investigate competitive supply chain networks, a supply chain network equilibrium (SCNE) model was proposed. Recently, particular attention has been paid to risk management of a supply chain. In equilibrium models, it is vital to consider players’ decisions and interdependence relations. Thus, we consider competitive supply chain networks with uncertainties in the other players’ strategies. In the proposed model, each player cannot know exactly the other players’ strategies, and they decide their strategy using the minimax principle (that is, assuming the worst case). We call it the robust SCNE model. We formulate the robust SCNE model as a variational inequality problem (VIP) in which the set associated with the VIP is constructed by second-order cone constraints. We show the existence and uniqueness of the equilibrium under mild assumptions. In addition, we give, in a special case, some relations between players’ strategies in the equilibrium and magnitudes of uncertainties. Finally, some numerical results are provided.

Suggested Citation

  • Tatsuya Hirano & Yasushi Narushima, 2019. "Robust Supply Chain Network Equilibrium Model," Transportation Science, INFORMS, vol. 53(4), pages 1196-1212, July.
    • Handle: RePEc:inm:ortrsc:v:53:y:2019:i:4:p:1196-1212
    DOI: 10.1287/trsc.2018.0843
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    References listed on IDEAS

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    Cited by:

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    2. Wang, Jiepeng & Zhou, Hong & Jin, Xiaodan, 2021. "Risk transmission in complex supply chain network with multi-drivers," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).

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