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作 者:Runze Liu Liguo Fei Jianing Mi
机构地区:[1]School of Management,Harbin Institute of Technology,Harbin,150001,China
出 处:《Computer Modeling in Engineering & Sciences》2023年第8期2039-2067,共29页工程与科学中的计算机建模(英文)
基 金:supported by National Social Science Foundation of China (Grant No.17ZDA030).
摘 要:Probabilistic linguistic term sets(PLTSs)are an effective tool for expressing subjective human cognition that offer advantages in the field ofmulti-attribute decision-making(MADM).However,studies have found that PLTSs have lost their ability to accurately capture the views of decision-makers(DMs)in certain circumstances,such as when the DM hesitates between multiple linguistic terms or the decision information is incomplete,thus affecting their role in the decision-making process.Belief function theory is a leading streamof thought in uncertainty processing that is suitable for dealing with the limitations of PLTS.Therefore,the purpose of this study is to extend PLTS to incorporate belief function theory.First,we provide the basic concepts of the extended PLTS(i.e.,belief-based PLTS)through case analyses.Second,the aggregation operator of belief-based PLTS is defined with the ordered weighted average(OWA)-based soft likelihood function,which is improved by considering the reliability of the information source.Third,to measure the magnitude of different belief-based PLTSs,the belief interval of singleton is calculated,and the comparison method of belief-based PLTS is constructed based on probabilities.On the basis of the preceding discussion,we further develop an emergency decision framework that includes several novel techniques,such as attribute weight determination and decision information aggregation.Finally,the usefulness of the framework is demonstrated through a case study,and its effectiveness is illustrated through a series of comparisons.
关 键 词:Probabilistic linguistic term sets Dempster-Shafer theory multi-attribute decision making emergency decisionmaking soft likelihood function disaster reduction education program selection
分 类 号:O225[理学—运筹学与控制论]
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