A Dynamic Reference Point Method for Emergency Response Under Hesitant Probabilistic Fuzzy Environment

Gao, Jie; Zhang, Xu; Liao, Huchang

doi:10.1007/s40815-017-0311-4

Cited by 72 publications

(59 citation statements)

References 48 publications

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“…The DMs’ risk preference attitudes have a great effect on the response of emergency events, which are usually ignored in most studies [ 1 , 2 , 4 ]. In this section, we employ the proposed HFLPU-TOPSIS method to solve a case concerning the selection of fire rescue plans, in which different parameters are used to reflect the DMs’ risk preference attitudes.…”

Section: Case Study: Selection Of Fire Rescue Plansmentioning

confidence: 99%

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Hesitant Fuzzy Linguistic Preference Utility Set and Its Application in Selection of Fire Rescue Plans

Liao

Zhang

et al. 2018

IJERPH

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Hesitant fuzzy linguistic term set provides an effective tool to represent uncertain decision information. However, the semantics corresponding to the linguistic terms in it cannot accurately reflect the decision-makers’ subjective cognition. In general, different decision-makers’ sensitivities towards the semantics are different. Such sensitivities can be represented by the cumulative prospect theory value function. Inspired by this, we propose a linguistic scale function to transform the semantics corresponding to linguistic terms into the linguistic preference values. Furthermore, we propose the hesitant fuzzy linguistic preference utility set, based on which, the decision-makers can flexibly express their distinct semantics and obtain the decision results that are consistent with their cognition. For calculations and comparisons over the hesitant fuzzy linguistic preference utility sets, we introduce some distance measures and comparison laws. Afterwards, to apply the hesitant fuzzy linguistic preference utility sets in emergency management, we develop a method to obtain objective weights of attributes and then propose a hesitant fuzzy linguistic preference utility-TOPSIS method to select the best fire rescue plan. Finally, the validity of the proposed method is verified by some comparisons of the method with other two representative methods including the hesitant fuzzy linguistic-TOPSIS method and the hesitant fuzzy linguistic-VIKOR method.

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Section: Case Study: Selection Of Fire Rescue Plansmentioning

confidence: 99%

“…To address emergency events, a range of decision methods were proposed within different contexts [ 1 , 2 , 3 , 4 ]. Although the methods in the above literature made some contributions to address emergency events, few studies considered the decision-makers’ (DMs’) risk preference attitudes in the decision process.…”

Section: Introductionmentioning

confidence: 99%

Hesitant Fuzzy Linguistic Preference Utility Set and Its Application in Selection of Fire Rescue Plans

Liao

Zhang

et al. 2018

IJERPH

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“…method to accommodate probabilistic hesitant fuzzy environment and applied the proposed method to the green supplier selection. Gao, Xu, and Liao (2017) presented a dynamic reference point method with probabilistic hesitant fuzzy information for emergency response. Xu and Zhou (2017) proposed the concept of P.H.F.E., which is often taken as the unit of P.H.F.S., and several probabilistic hesitant fuzzy aggregation operators were put forward to fuse probabilistic hesitant fuzzy information.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic hesitant fuzzy multiple attribute decision-making based on regret theory for the evaluation of venture capital projects

Wang

Zhang

Jia-shu

2020

Economic Research-Ekonomska Istraživanja

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The selection of venture capital investment projects is one of the most important decision-making activities for venture capitalists. Due to the complexity of investment market and the limited cognition of people, most of the venture capital investment decision problems are highly uncertain and the venture capitalists are often bounded rational under uncertainty. To address such problems, this article presents an approach based on regret theory to probabilistic hesitant fuzzy multiple attribute decision-making. Firstly, when the information on the occurrence probabilities of all the elements in the probabilistic hesitant fuzzy element (P.H.F.E.) is unknown or partially known, two different mathematical programming models based on water-filling theory and the maximum entropy principle are provided to handle these complex situations. Secondly, to capture the psychological behaviours of venture capitalists, the regret theory is utilised to solve the problem of selection of venture capital investment projects. Finally, comparative analysis with the existing approaches is conducted to demonstrate the feasibility and applicability of the proposed method.

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“…To alleviate the issue, Xu and Zhou [10] put forward the probabilistic hesitant fuzzy set (PHFS), which associates occurrence probability value for each HFE. Motivated by the power of PHFS, researchers widely explored the idea for multi-attribute decision-making (MADM) [11][12][13][14][15][16][17]. Though PHFS alleviates the weakness of HFS to some extent, still the elicitation of occurrence probability is prone to imprecision and inaccuracy.…”

Section: Introductionmentioning

confidence: 99%

Interval-Valued Probabilistic Hesitant Fuzzy Set Based Muirhead Mean for Multi-Attribute Group Decision-Making

et al. 2019

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As a powerful generalization to fuzzy set, hesitant fuzzy set (HFS) was introduced, which provided multiple possible membership values to be associated with a specific instance. But HFS did not consider occurrence probability values, and to circumvent the issue, probabilistic HFS (PHFS) was introduced, which associates an occurrence probability value with each hesitant fuzzy element (HFE). Providing such a precise probability value is an open challenge and as a generalization to PHFS, interval-valued PHFS (IVPHFS) was proposed. IVPHFS provided flexibility to decision makers (DMs) by associating a range of values as an occurrence probability for each HFE. To enrich the usefulness of IVPHFS in multi-attribute group decision-making (MAGDM), in this paper, we extend the Muirhead mean (MM) operator to IVPHFS for aggregating preferences. The MM operator is a generalized operator that can effectively capture the interrelationship between multiple attributes. Some properties of the proposed operator are also discussed. Then, a new programming model is proposed for calculating the weights of attributes using DMs’ partial information. Later, a systematic procedure is presented for MAGDM with the proposed operator and the practical use of the operator is demonstrated by using a renewable energy source selection problem. Finally, the strengths and weaknesses of the proposal are discussed in comparison with other methods.

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A Dynamic Reference Point Method for Emergency Response Under Hesitant Probabilistic Fuzzy Environment

Cited by 72 publications

References 48 publications

Hesitant Fuzzy Linguistic Preference Utility Set and Its Application in Selection of Fire Rescue Plans

Hesitant Fuzzy Linguistic Preference Utility Set and Its Application in Selection of Fire Rescue Plans

Probabilistic hesitant fuzzy multiple attribute decision-making based on regret theory for the evaluation of venture capital projects

Interval-Valued Probabilistic Hesitant Fuzzy Set Based Muirhead Mean for Multi-Attribute Group Decision-Making

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