A multi-stage conflict style large group emergency decision-making method

Cai, Chenguang; Xu, Xuanhua; Wang, Pei; Chen, Xiaohong

doi:10.1007/s00500-016-2155-5

Cited by 48 publications

(30 citation statements)

References 22 publications

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“…Although these existing methods can deal with the large-scale evaluation information effectively, their operation processes are relatively complex, which will make the clustering process of large-scale decision makers more difficult to manage in the GSS problem. In contrast, the clustering approach based on the similarity measure is more appropriate to solve this situation due to its features of simplicity and effectiveness [61]. Therefore, we can develop the similarity measure of q-RIVOFS to construct a novel clustering approach for implementing the clustering process in the large-scale GSS problem.…”

Section: Clustering Process Of Large-scale Decision Makersmentioning

confidence: 99%

“…Inspired by the literature [61], similarity measure is the basis of the clustering approach, thus, we define the similarity measure between different q-RIVOFNs as follows:…”

Section: Clustering Process Of Large-scale Decision Makersmentioning

confidence: 99%

“…Another important segment is how to determine an appropriate threshold, which can guide the large-scale decision makers to be separated into several subgroups. Based on the literature [61,62], we can utilize the majority principle to construct the clustering threshold as…”

Section: Clustering Process Of Large-scale Decision Makersmentioning

confidence: 99%

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Large-Scale Green Supplier Selection Approach under a Q-Rung Interval-Valued Orthopair Fuzzy Environment

et al. 2019

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As enterprises pay more and more attention to environmental issues, the green supply chain management (GSCM) mode has been extensively utilized to guarantee profit and sustainable development. Green supplier selection (GSS), which is a key segment of GSCM, has been investigated to put forward plenty of GSS approaches. At present, enterprises prefer to construct the large-scale teams of decision makers to obtain the more reasonable ranking results during GSS process. However, the existing methods pay little attention to the large-scale GSS procedure. To investigate the GSS issue with a large-scale group of decision makers, a new GSS approach under a q-rung interval-valued orthopair fuzzy environment is developed. The q-rung interval-valued orthopair fuzzy numbers are introduced to describe the evaluation information of green suppliers. Combined with a clustering approach and several clustering principles, the large-scale decision makers are divided into several subgroups. Next, the similarity measures between the evaluation matrices are computed to determine the weights of subgroups, and the collective evaluation information can be obtained using the q-rung interval-valued orthopair fuzzy aggregation operator. According to the weighted entropy measure, the weights of criteria are calculated; then, the q-rung interval-valued orthopair fuzzy multi-objective optimization on the basis of ratio analysis plus the full multiplicative form (q-RIVOF-MULTIMOORA) method is constructed to determine the best green supplier. At last, a practical GSS example is applied to show the feasibility of the proposed approach, and the sensitivity and comparative analyses indicate that for the large-scale GSS issues, the proposed approach can obtain the more robust and reasonable ranking results.

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Section: Clustering Process Of Large-scale Decision Makersmentioning

confidence: 99%

“…Inspired by the literature [61], similarity measure is the basis of the clustering approach, thus, we define the similarity measure between different q-RIVOFNs as follows:…”

Section: Clustering Process Of Large-scale Decision Makersmentioning

confidence: 99%

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Large-Scale Green Supplier Selection Approach under a Q-Rung Interval-Valued Orthopair Fuzzy Environment

et al. 2019

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“…Therefore, our study presents a novel solution adaption model using the CS algorithm. In the adaption step, a new problem scenario x i t þ 1 ð Þ is given by formula (12).…”

Section: Cbr Adaptation Based On Cuckoo Search (Cs) Algorithmmentioning

confidence: 99%

Novel Case-Based Reasoning System for Public Health Emergencies

Duan¹,

Jiao²

2021

RMHP

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Purpose: Several threatening infectious diseases, including influenza, Ebola, SARS, and COVID-19, have affected human society over the past decades. These disease outbreaks naturally inspire a demand for sustained and advanced safety and suppression measures. To protect public health and safety, further research developments on emergency analysis methods and approaches for effective emergency treatment generation are urgently needed to mitigate the severity of the pandemic and save lives. Methods: To address these issues, a novel case-based reasoning (CBR) system is proposed using three phases. In the first phase, the similarity between the current case and the historical cases is calculated under a variety of heterogeneous information. In the second phase, a filter approach based on grey clustering analysis is created to retrieve relevant cases. In the third phase, the cases retrieved are taken as initial host nests in a cuckoo search (CS) algorithm, and our system searches an optimal solution through iteration of this algorithm. Results: The proposed model is compared with a CBR method improved by particle swarm optimization (PSO) and a CBR method improved by a differential evolution algorithm (DE), to confirm the efficiency of our CS algorithm in adapting solutions for public health emergencies. The results show that the proposed model is better than the existing algorithms. Conclusion: The proposed model improves the speed of case retrieval using grey clustering and increases solution accuracy with CS algorithms. The present research can contribute to government, CDC, and infectious disease emergency management fields with regard to the implementation of fast and accurate public biohazard prevention and control measures based on a variety of heterogeneous information.

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“…When an emergency event occurs, to reduce casualties and property losses and eliminate potential derivative hazards, a reasonable and effective emergency solution should be made within a short period of time (Sun, Ma, & Chen, ; Xu, Huang, & Chen, 2019). However, because emergency events are characterized by randomness, complexity, and uncertainty, it is difficult for conventional emergency measures to address them (Cai, Xu, Wang, & Chen, ; Ding & Liu, ). Therefore, the researches on emergency decision making (EDM) problems have gained increasingly attentions from both scholars and practitioners recently (Ding, Liu, & Shi, ; Gao, Xu, Ren, & Liao, ; Li & Wei, ; Y. Liang, Tu, Ju, & Shen, ; Zhou, Wang, Qin, Chai, & Gómez Muñoz, ).…”

Section: Introductionmentioning

confidence: 99%

Emergency decision making with extended axiomatic design approach under picture fuzzy environment

2019

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Timely and effective emergency decision making (EDM) is the key to control the spread of disasters and reduce the casualties and property losses caused by emergencies. However, due to limited time and insufficient data, it is difficult for decision makers to provide accurate information about emergency incidents. Moreover, the EDM problems become complicated and unstructured requiring the deployment of advanced mathematical techniques to derive the most acceptable response. In this paper, we propose a new EDM approach by using picture fuzzy sets and axiomatic design technique for determining the optimal rescue plan to reduce the damages of emergencies. The contribution of this paper is to apply the picture fuzzy sets to handle the uncertainty and ambiguity of decision makers' assessments on emergency alternatives, apply the picture fuzzy hybrid averaging operator to aggregate decision makers' opinions into a group evaluation matrix and extend the axiomatic design technique to identify the best emergency solution for EDM. Finally, a real example is provided, and the result is compared with existing methods to demonstrate the feasibility and practicability of our proposed EDM approach.

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A multi-stage conflict style large group emergency decision-making method

Cited by 48 publications

References 22 publications

Large-Scale Green Supplier Selection Approach under a Q-Rung Interval-Valued Orthopair Fuzzy Environment

Large-Scale Green Supplier Selection Approach under a Q-Rung Interval-Valued Orthopair Fuzzy Environment

Novel Case-Based Reasoning System for Public Health Emergencies

Emergency decision making with extended axiomatic design approach under picture fuzzy environment

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