Robust consensus models based on minimum cost with an application to marketing plan

Han, Yefan; Qu, Shaojian; Wu, Zhongqing; Huang, Ripeng

doi:10.3233/jifs-190863

Cited by 39 publications

(15 citation statements)

References 50 publications

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“…To deal with the uncertainty, different methods and theories have been put forward for processing the consensus over the past few years. Related research on consensus issue is mainly divided into five categories, including interval analysis (see Li et al (2017); Gong et al (2018)), fuzzy sets (see Xu and Wu (2013); ), probability theory (see Xie et al (2018); Tan et al (2018); Wang et al (2021)), uncertainty theory (see Gong et al (2020Gong et al ( , 2018), and robust optimization method (see Lu et al (2020); Han et al (2019); Qu et al (2020)). In particular, researchers may have trouble obtaining a precise probability for a specific decision problem.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

2021

Group Decis Negot

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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.

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Section: Introductionmentioning

confidence: 99%

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

Zhang

2021

Group Decis Negot

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“…In order to establish the robust counterpart of the uncertain entropy minimization model, we assuming that the uncertain set U is given as with a closed and convex perturbation set Z . According to Ben-Tal et al (2004), Huang et al (2019) and Han et al (2019), we lose nothing when assuming from the very beginning that the set U are closed and convex. In this paper, we only consider uncertain parameter c due to parameter b.…”

Section: Minimum Entropy Model and Robust Optimizationmentioning

confidence: 99%

Uncertainty in the prediction and management of CO2 emissions: a robust minimum entropy approach

Cai

et al. 2020

Nat Hazards

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CO 2 emission control is one of the most vital parts of environment management. China owns the largest CO 2 emission in the world. For the sake of clarifying China's emission sharing responsibilities and set emission reduction targets, a considerable number of scholars have worked to project China's embodied CO 2 emission. Single Regional Input-output (SRIO) model is widely used for investigating CO 2 emission issues. Considering the ubiquitous time lag of input-output data, entropy optimization model is introduced to estimate SRIO tables. However, the uncertainty in the corresponding model parameters necessarily has a serious impact on the estimation results. To consider the impact of uncertainties, we introduce robust optimization into entropy minimization model for SRIO table estimation. Based on three different uncertainty sets, we constructed three robust entropy minimization models to construct 2016 China's SRIO tables and calculate China's embodied CO 2 emission based on those tables. The estimation results show that the model based on the ball uncertainty set has the best performance with less uncertainty, while the model based on the budgeted uncertainty set performances more 'robust' facing greater uncertainty, which means its performance is less volatile at different levels of uncertainty. Moreover, the embodied carbon emission is predicted to reach 9632.57 Mt CO 2 . The top emitter is the sector of supply of electricity, heating and water, which accounts for more than 40% of total CO 2 emission.

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“…It can be seen that it is necessary to consider robust optimization when the environment is uncertain. Robust optimization is now widely used in many fields [22][23][24], such as the consensus field [25][26][27][28][29][30], supply chain management [31,32], the energy field [33], etc. Some scholars have combined robust optimization and DEA [34].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Robust Data Envelopment Analysis for Evaluating the Carbon Emissions Efficiency of Provinces in China

et al. 2022

Sustainability

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To combat global warming, China proposed the “dual carbon” policy in 2020. In this context, it becomes crucial to improve carbon emissions efficiency. Currently, some scholars have utilized data envelopment analysis (DEA) to study carbon emissions efficiency. However, uncertainty about climate and government economic policy is ignored. This paper establishes a robust DEA model to reduce uncertainty and improve robustness. First, robust optimization theory is combined with DEA to establish the robust DEA model. Second, considering three uncertainty sets (box set, ellipsoid set, and polyhedron set), a robust DEA model for different situations is considered. Finally, to address the problem of over-conservatism in robust optimization, this paper applies the data-driven robust DEA model to further analyze the carbon emissions efficiency of China. The results of the data-driven robust DEA model suggest that the government should focus on coordinated regional development, promote the transformation and upgrading of the energy structure, innovate in green technology, and advocate for people to live a green and low-carbon lifestyle.

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Robust consensus models based on minimum cost with an application to marketing plan

Cited by 39 publications

References 50 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

Uncertainty in the prediction and management of CO2 emissions: a robust minimum entropy approach

Data-Driven Robust Data Envelopment Analysis for Evaluating the Carbon Emissions Efficiency of Provinces in China

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