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Consensus Modeling with Asymmetric Cost Based on Data-Driven Robust Optimization

Author

Listed:
  • Shaojian Qu

    (Nanjing University of Information Science and Technology
    University of Shanghai for Science and Technology)

  • Yefan Han

    (University of Shanghai for Science and Technology)

  • Zhong Wu

    (University of Shanghai for Science and Technology)

  • Hassan Raza

    (University of Shanghai for Science and Technology)

Abstract

The robust optimization method has progressively become a research hot spot as a valuable means for dealing with parameter uncertainty in optimization problems. Based on the asymmetric cost consensus model, this paper considers the uncertainties of the experts’ unit adjustment costs under the background of group decision making. At the same time, four uncertain level parameters are introduced. For three types of minimum cost consensus models with direction restrictions, including MCCM-DC, $$\varepsilon $$ ε -MCCM-DC and threshold-based (TB)-MCCM-DC, the robust cost consensus models corresponding to four types of uncertainty sets (Box set, Ellipsoid set, Polyhedron set and Interval-Polyhedron set) are established. Sensitivity analysis is carried out under different parameter conditions to determine the robustness of the solutions obtained from robust optimization models. The robust optimization models are then compared to the minimum cost models for consensus. The example results show that the Interval-Polyhedron set’s robust models have the smallest total costs and strongest robustness. Decision makers can choose the combination of uncertainty sets and uncertain levels according to their risk preferences to minimize the total cost. Finally, in order to reduce the conservatism of the classical robust optimization method, the pricing information of the new product MACUBE 550 is used to build a data-driven robust optimization model. Ellipsoid uncertainty set is proved to better trade-off the average performance and robust performance through different measurement indicators. Therefore, the uncertainty set can be selected according to the needs of the group.

Suggested Citation

  • Shaojian Qu & Yefan Han & Zhong Wu & Hassan Raza, 2021. "Consensus Modeling with Asymmetric Cost Based on Data-Driven Robust Optimization," Group Decision and Negotiation, Springer, vol. 30(6), pages 1395-1432, December.
  • Handle: RePEc:spr:grdene:v:30:y:2021:i:6:d:10.1007_s10726-020-09707-w
    DOI: 10.1007/s10726-020-09707-w
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    References listed on IDEAS

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    1. Zaiwu Gong & Chao Xu & Francisco Chiclana & Xiaoxia Xu, 2017. "Consensus Measure with Multi-stage Fluctuation Utility Based on China’s Urban Demolition Negotiation," Group Decision and Negotiation, Springer, vol. 26(2), pages 379-407, March.
    2. Shishebori, Davood & Yousefi Babadi, Abolghasem, 2015. "Robust and reliable medical services network design under uncertain environment and system disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 77(C), pages 268-288.
    3. Danqin Yang & Tiaojun Xiao & Tsan-Ming Choi & T.C.E. Cheng, 2018. "Optimal reservation pricing strategy for a fashion supply chain with forecast update and asymmetric cost information," International Journal of Production Research, Taylor & Francis Journals, vol. 56(5), pages 1960-1981, March.
    4. Yejun Xu & Dou Rui & Huimin Wang, 2017. "A dynamically weight adjustment in the consensus reaching process for group decision-making with hesitant fuzzy preference relations," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(6), pages 1311-1321, April.
    5. Mahadevan, B. & Hazra, Jishnu & Jain, Tarun, 2017. "Services outsourcing under asymmetric cost information," European Journal of Operational Research, Elsevier, vol. 257(2), pages 456-467.
    6. Cheng, Dong & Zhou, Zhili & Cheng, Faxin & Zhou, Yanfang & Xie, Yujing, 2018. "Modeling the minimum cost consensus problem in an asymmetric costs context," European Journal of Operational Research, Elsevier, vol. 270(3), pages 1122-1137.
    7. A. L. Soyster, 1973. "Technical Note—Convex Programming with Set-Inclusive Constraints and Applications to Inexact Linear Programming," Operations Research, INFORMS, vol. 21(5), pages 1154-1157, October.
    8. Chassein, André & Dokka, Trivikram & Goerigk, Marc, 2019. "Algorithms and uncertainty sets for data-driven robust shortest path problems," European Journal of Operational Research, Elsevier, vol. 274(2), pages 671-686.
    9. Dong, Yucheng & Xu, Yinfeng & Li, Hongyi & Feng, Bo, 2010. "The OWA-based consensus operator under linguistic representation models using position indexes," European Journal of Operational Research, Elsevier, vol. 203(2), pages 455-463, June.
    10. A. Ben-Tal & A. Nemirovski, 1998. "Robust Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 23(4), pages 769-805, November.
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    Cited by:

    1. Shaojian Qu & Yuting Xu & Ying Ji & Can Feng & Jinpeng Wei & Shan Jiang, 2022. "Data-Driven Robust Data Envelopment Analysis for Evaluating the Carbon Emissions Efficiency of Provinces in China," Sustainability, MDPI, vol. 14(20), pages 1-26, October.
    2. Li, Huanhuan & Ji, Ying & Ding, Jieyu & Qu, Shaojian & Zhang, Huijie & Li, Yuanming & Liu, Yubing, 2024. "Robust two-stage optimization consensus models with uncertain costs," European Journal of Operational Research, Elsevier, vol. 317(3), pages 977-1002.
    3. Xuyuan Zhang & Hailin Liang & Shaojian Qu, 2024. "Robust Consensus Modeling: Concerning Consensus Fairness and Efficiency with Uncertain Costs," Mathematics, MDPI, vol. 12(8), pages 1-31, April.

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