A linguistic entropy weight method and its application in linguistic multi-attribute group decision making

He, Yufei; Guo, Hongwei; Jin, Maozhu; Ren, Peiyu

doi:10.1007/s11071-015-2595-y

Cited by 90 publications

(44 citation statements)

References 7 publications

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“…Entropy weight method [28,29] is an objective weight coefficient distribution method, which mainly determines the weight coefficient through the change level of each index. Firstly, on the basis of judging the degree of difference among each index, the entropy weight of each index is calculated according to the information entropy [30].…”

Section: Weight Coefficient Calculation Based On Entropy Weight Methodsmentioning

confidence: 99%

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Research on Multi-Objective Optimization and Control Algorithms for Automatic Train Operation

Liu

Wang

2019

Energies

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The automatic train operation (ATO) system of urban rail trains includes a two-layer control structure: upper-layer control and lower-layer control. The upper-layer control is to optimize the target speed curve of ATO, and the lower-layer control is the tracking by the urban rail train of the optimal target speed curve generated by the upper-layer control according to the tracking control strategy of ATO. For upper-layer control, the multi-objective model of urban rail train operation is firstly built with energy consumption, comfort, stopping accuracy, and punctuality as optimization indexes, and the entropy weight method is adopted to solve the weight coefficient of each index. Then, genetic algorithm (GA) is used to optimize the model to obtain an optimal target speed curve. In addition, an improved genetic algorithm (IGA) based on directional mutation and gene modification is proposed to improve the convergence speed and optimization effect of the algorithm. The stopping and speed constraints are added into the fitness function in the form of penalty function. For the lower-layer control, the predictive speed controller is designed according to the predictive control principle to track the target speed curve accurately. Since the inflection point area of the target speed curve is difficult to track, the softness factor in the predictive model needs to be adjusted online to improve the control accuracy of the speed. For this paper, we mainly improve the optimization and control algorithms in the upper and lower level controls of ATO. The results show that the speed controller based on predictive control algorithm has better control effect than that based on the PID control algorithm, which can meet the requirements of various performance indexes. Thus, the feasibility of predictive control algorithm in an ATO system is also verified.

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Section: Weight Coefficient Calculation Based On Entropy Weight Methodsmentioning

confidence: 99%

“…; q j is used to constrain the error change; and r j is used to constrain the change of the control increment. Substitute Equation (28) into the function J, we get…”

Section: Rolling Optimizationmentioning

confidence: 99%

Research on Multi-Objective Optimization and Control Algorithms for Automatic Train Operation

Liu

Wang

2019

Energies

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“…Since the chance of getting tails is equal to the chance of getting heads, there is no way to predict what will come next. A smaller value of entropy indicates that there is less useful information content [27][28][29][30][31]. In a multiattribute decision-making problem, we need to assign a larger weight to attribute with more useful information rather than the attribute with greater uncertainty.…”

Section: Data Normalizationmentioning

confidence: 99%

An Entropy-Based Self-Adaptive Node Importance Evaluation Method for Complex Networks

2020

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Identifying important nodes in complex networks is essential in disease transmission control, network attack protection, and valuable information detection. Many evaluation indicators, such as degree centrality, betweenness centrality, and closeness centrality, have been proposed to identify important nodes. Some researchers assign different weight to different indicator and combine them together to obtain the final evaluation results. However, the weight is usually subjectively assigned based on the researcher’s experience, which may lead to inaccurate results. In this paper, we propose an entropy-based self-adaptive node importance evaluation method to evaluate node importance objectively. Firstly, based on complex network theory, we select four indicators to reflect different characteristics of the network structure. Secondly, we calculate the weights of different indicators based on information entropy theory. Finally, based on aforesaid steps, the node importance is obtained by weighted average method. The experimental results show that our method performs better than the existing methods.

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“…On the other hand, there are some objective methods for determining weight vector. For instance, based on the information entropy (Xia and Xu, ; He et al., ), maximizing deviation (Wu and Fang, ; Şahin and Liu, ), or maximizing closeness coefficient method (Ding et al., ) and so forth, by constructing optimization model or using algebra formulas, the objective weight can be derived. In this example, we suppose that the criterion weight vector is

W = {(0.09, 0.55, 0.27, 0.09)}^{T}

, which was derived from a subjective method.…”

Section: Applicationsmentioning

confidence: 99%

Stochastic multiple‐criteria decision making with 2‐tuple aspirations: a method based on disappointment stochastic dominance

Liang

Jiang

Лю

2017

Int Trans Operational Res

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Considering the disappointment aversion behavior of decision makers, we define three types of disappointment stochastic dominance rules and disappointment stochastic dominance degree between two stochastic variables. Then we prove some important properties of disappointment stochastic dominance. With respect to the stochastic multiple‐criteria decision‐making (SMCDM) problem with criterion 2‐tuple aspirations, a novel method based on disappointment stochastic dominance is proposed. To begin with, based on the 2‐tuple aspiration on each criterion, for the situations of discrete and continuous stochastic criterion values, the aspiration alternative and its cumulative distribution functions of stochastic criterion values are constructed. Next, the disappointment stochastic dominance relation between each alternative and aspiration alternative is determined based on the definition of disappointment stochastic dominance. Further, the corresponding disappointment stochastic dominance degree is calculated. And the overall disappointment stochastic dominance degree of each alternative over the aspiration alternative is calculated to obtain the ranking result. Finally, an example of selecting desirable computer development project(s) is given to illustrate the feasibility and validity of the proposed method.

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A linguistic entropy weight method and its application in linguistic multi-attribute group decision making

Cited by 90 publications

References 7 publications

Research on Multi-Objective Optimization and Control Algorithms for Automatic Train Operation

Research on Multi-Objective Optimization and Control Algorithms for Automatic Train Operation

An Entropy-Based Self-Adaptive Node Importance Evaluation Method for Complex Networks

Stochastic multiple‐criteria decision making with 2‐tuple aspirations: a method based on disappointment stochastic dominance

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