Consensus Modeling with Asymmetric Cost Based on Data-Driven Robust Optimization
- PDF / 1,976,554 Bytes
- 38 Pages / 439.37 x 666.142 pts Page_size
- 28 Downloads / 175 Views
Consensus Modeling with Asymmetric Cost Based on Data‑Driven Robust Optimization Shaojian Qu1,2 · Yefan Han2 · Zhong Wu2 · Hassan Raza2 Accepted: 6 September 2020 © Springer Nature B.V. 2020
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,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. Keywords Group decision making · Minimum cost consensus · Robust optimization · Data-driven optimization · Asymmetric cost
* Shaojian Qu [email protected] Yefan Han [email protected] 1
Nanjing University of Information Science and Technology, Nanjing 210044, China
2
University of Shanghai for Science and Technology, Shanghai 200093, China
13
Vol.:(0123456789)
S. Qu et al.
1 Introduction Assessment and decision-making will occur in diverse fields, such as politics, economy, management and even daily life. It is also essential to attain a group consensus which is acceptable to most participants. However, due to different social experiences, national culture and educational background of participants, they often have different views and interpretations on the same thing, then they make different decisions. Group decision making (GDM) is to study how to coordinate different decisions to achieve the final group opinions. The main goal of GDM is that all decision-makers (DMs) can reach group consensus. In the consensus-reaching processes (CRPs), DMs published and modified their opinions according to the moderator’s guidance and finally managed to unify and reach a consensus. CR
Data Loading...
