Robust optimization strategies for seller based on uncertainty sets in context of sequential auction

Gang, Ma; Zheng, Junjun; Wei, Ju; Wang, Shilei; Han, Yefan

doi:10.1016/j.amc.2020.125650

Cited by 5 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As an effective uncertainty optimization tool, robust optimization focuses on achieving optimal results in worst-case situations. In the context of sequential auction, Ma et al (2021) provided a robust optimization method to find the maximum revenue under different uncertainty sets. Therefore, inspired by this kind of robust modeling techniques, future research on the robust and two-stage mean-risk stochastic consensus problem is a very meaningful research direction.…”

Section: Discussionmentioning

confidence: 99%

Risk-Averse Two-Stage Stochastic Minimum Cost Consensus Models with Asymmetric Adjustment Cost

Zhang

2021

Group Decis Negot

View full text Add to dashboard Cite

In the process of reaching consensus, it is necessary to coordinate different views to form a general group opinion. However, there are many uncertain factors in this process, which has brought different degrees of influence in group decision-making. Besides, these uncertain elements bring the risk of loss to the whole process of consensus building. Currently available models not account for these two aspects. To deal with these issues, three different modeling methods for constructing the two-stage mean-risk stochastic minimum cost consensus models (MCCMs) with asymmetric adjustment cost are investigated. Due to the complexity of the resulting models, the L-shaped algorithm is applied to achieve an optimal solution. In addition, a numerical example of a peer-to-peer online lending platform demonstrated the utility of the proposed modeling approach. To verify the result obtained by the L-shaped algorithm, it is compared with the CPLEX solver. Moreover, the comparison results show the accuracy and efficiency of the given method. Sensitivity analyses are undertaken to assess the impact of risk on results. And in the presence of asymmetric cost, the comparisons between the new proposed risk-averse MCCMs and the two-stage stochastic MCCMs and robust consensus models are also given.

show abstract

Section: Discussionmentioning

confidence: 99%

Risk-Averse Two-Stage Stochastic Minimum Cost Consensus Models with Asymmetric Adjustment Cost

Zhang

2021

Group Decis Negot

View full text Add to dashboard Cite

show abstract

“…So, combining optimization theory with cost consensus problems for group decision-making in uncertain environments is an important branch of research. Stochastic Programming (SP) and Robust Optimization (RO) are two research ideas that exist in existing research approaches to dispose of uncertainties in cost consensus problems [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Distributionally Robust Two-Stage Minimum Asymmetric Adjustment Cost Consensus Model with Risk Aversion

Dai,

Ye,

et al. 2024

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

Global warming, mainly caused by human activities, demands urgent reduction of greenhouse gas emissions. Establishing a carbon market central to this effort involves the allocation of carbon emission quotas. The uncertainty of consensus cost in the carbon market will bring the risk of loss to the whole consensus process. In this paper, we focus on solving the problem of group consensus decision with risk averse decision maker. First, three new distributionally robust two-stage minimum asymmetric adjustment cost consensus models based on conditional value at risk (CVaR) are proposed. Considering that it is difficult to obtain historical decision-making data with risk in the carbon market, a novel box ambiguous set and a polyhedron ambiguous set are constructed, respectively. The risk expectation cost of group consensus decision-making problem under the worst-case condition is measured. Then, a computable linear equivalent form of the proposed model is derived in order to facilitate calculation. Finally, numerical cases based on carbon emission quotas are carried out. The numerical results show that the consensus cost of this method is better than the results under the stochastic programming method, and it brings new solutions to the group decision-making progress in the allocation of carbon emission quotas.

show abstract

Transfer-stable aggregation functions: Applications, challenges, and emerging trends

Kurač

2023

Decision Analytics Journal

View full text Add to dashboard Cite

Robust optimization strategies for seller based on uncertainty sets in context of sequential auction

Cited by 5 publications

References 31 publications

Risk-Averse Two-Stage Stochastic Minimum Cost Consensus Models with Asymmetric Adjustment Cost

Risk-Averse Two-Stage Stochastic Minimum Cost Consensus Models with Asymmetric Adjustment Cost

Distributionally Robust Two-Stage Minimum Asymmetric Adjustment Cost Consensus Model with Risk Aversion

Transfer-stable aggregation functions: Applications, challenges, and emerging trends

Contact Info

Product

Resources

About