Maintenance strategy selection with risky evaluations using <scp>RAHP</scp>

Seiti, Hamidreza; Tagipour, Razieh; Hafezalkotob, Ashkan; Asgari, Farhad

doi:10.1002/mcda.1618

Cited by 35 publications

(25 citation statements)

References 44 publications

(41 reference statements)

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“…Decision making under uncertainty exists everywhere . In this section, the application of

P O W A

operator in likelihood function of uncertain decision making is given and the differences between different formulations of likelihood function are shown.…”

Section: A Case Study and Results Analysismentioning

confidence: 99%

A new soft likelihood function based on power ordered weighted average operator

Song

Deng

2019

Int J Intell Syst

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The likelihood function is widely used in data processing but the classical likelihood function is too strict to deal with data with extreme values in real applications.To address this issue, a soft likelihood function based on power ordered weighted average (POWA) operator for probability distribution is proposed. Compared with the existing soft likelihood functions, one of the main features of the proposed likelihood function is that the weighted mean is displayed by the geometric mean of the weighted probability products. As a result, the effect on final result caused by extreme data can be efficiently decreased. Numerical examples are used to illustrate the efficiency of the proposed soft likelihood function. K E Y W O R D S geometric mean, likelihood function, OWA operator, POWA operator, soft likelihood function | INTRODUCTIONDealing with uncertainty information plays an important role in real applications. 1-3 Many math tools, such as fuzzy sets, 4-6 rough sets, 7 Z-numbers, 8-11 belief structures, 12-14 D numbers 15-17 entropy function, 18,19 and evidence theory, 20-22 are presented to handle different types of uncertainty information. Among these tools, probability is heavily studied and is widely used in many applications, including decision making, 23-26 estimation, 27,28 and uncertain measurement. [29][30][31][32] One of the most used models in probability is the likelihood function, which is used when data are available to describe plausibility of a parameter value. [33][34][35] However, it is very strict in real applications since the original likelihood function made up with the probability product is too strict to show the compatibility probabilities for the different pieces of evidence since any piece of probabilities, which are low-value would greatly reduce the likelihood function.How to cite this article: Song Y, Deng Y. A new soft likelihood function based on power ordered weighted average operator. Int J Intell

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“…Decision making under uncertainty exists everywhere . In this section, the application of

P O W A

operator in likelihood function of uncertain decision making is given and the differences between different formulations of likelihood function are shown.…”

Section: A Case Study and Results Analysismentioning

confidence: 99%

A new soft likelihood function based on power ordered weighted average operator

Song

Deng

2019

Int J Intell Syst

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“…The AHP method is based on an additive weighting process, in which several relevant criteria and alternatives are exhibited through their relative importance. The AHP approach has been extensively applied by academics and professionals in many fields and problems, mainly in engineering fields such as transport engineering [24][25][26][27][28], industrial engineering [29], civil engineering [30] and in many other engineering fields [31][32][33]. However, the AHP approach creates and deals with a very unbalanced scale of judgment, and it does not take into account the uncertainty associated with the mapping of human judgment to a number by natural language [34]; the ranking of the AHP method is rather imprecise, and subjective judgment by perception, evaluation, improvement and selection based on the preference of decision-makers has a great influence on the AHP results.…”

Section: Methodsmentioning

confidence: 99%

Sustainable Urban Transport Development by Applying a Fuzzy-AHP Model: A Case Study from Mersin, Turkey

Moslem

Duleba

2019

Urban Science

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Sustainable development decisions generally require citizen participation in the decision process to avoid public resistance and objections in the long term. Because of the involvement of non-experts, the uncertainty of the decision is increased, and this must be considered in the decision-making process. This paper aims to introduce a sustainable urban transport development problem in which citizens are involved to allow them to express their preferences for improving certain elements of the public bus system. To mitigate the uncertainty of the non-expert evaluations, a fuzzy-analytic hierarchy process (AHP) model has been created and applied. Since the objective of the research is to provide a suitable framework for transport development tenders, only the criteria weights have to be determined; thus, an alternative level has not been applied. The model has been tested on the urban bus transport system of a large Turkish city: Mersin. Based on the application, citizen preference weights could be associated with certain elements of the supply quality; thus, government development source allocation decisions could be supported. The fuzzy-AHP model ensures that the final development implications will meet public demand for bus system improvement in the city.

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“…The D‐S evidence theory was first proposed by Dempster in 1967 and later developed by his student Shafer in 1976 as an imprecise reasoning theory, which is widely promoted . The theory was first applied to expert systems, and it was gradually applied in the fields of information fusion, intelligence analysis, evidential reasoning, and multiattribute decision analysis . As an indeterminate reasoning method which satisfies the conditions weaker than Bayesian probability theory; it has the ability to directly express “uncertainty” and “don't know.” Therefore, the D‐S evidence theory is flexible and convenient for describing uncertainties.…”

Section: Introductionmentioning

confidence: 99%

An association coefficient of a belief function and its application in a target recognition system

Pan

Deng

2019

Int J Intell Syst

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The conflict problem in D-S evidence theory has attracted the attention of many scholars. Conflict coefficients are proposed to describe conflicts between bodies of evidence. The association coefficient as the opposite of the conflict coefficient is also used to measure the conflict. The larger the association coefficient, the smaller the conflict degree, and the higher the similarity between the evidence bodies, and vice versa.In this paper, the degree of association is defined by Deng Entropy, and a new association coefficient is proposed based on the basic inequality. The nature of the new association coefficient and conflict coefficients is explored using examples. Finally, the association coefficient combined with the D-S combination rule is applied to the target recognition system, and accurate results are obtained.association coefficient, conflict coefficients, conflict problem, Deng entropy, D-S evidence theory, target recognition system | INTRODUCTIONThe D-S evidence theory was first proposed by Dempster 1 in 1967 and later developed by his student Shafer 2 in 1976 as an imprecise reasoning theory, which is widely promoted. [3][4][5][6] The theory was first applied to expert systems, 7-9 and it was gradually applied in the fields of information fusion, 10-12 intelligence analysis, 9,13-16 evidential reasoning, [17][18][19] and multiattribute decision analysis. [20][21][22][23][24] As an indeterminate reasoning method which satisfies the conditions weaker than Bayesian probability theory; it has the ability to directly express "uncertainty" 25 and "don't know." 26 Therefore, the D-S evidence theory is flexible and convenient for describing uncertainties. However, D-S evidence theory has produced counterintuitive results in the fusion of a highly conflicting example proposed by Zadeh. 27 Therefore, the issue of conflict between bodies of evidence has attracted more and more researchers so far. [28][29][30][31][32] Domestic and foreign scholars have proposed a large number of improved methods, which can be divided into two major categories: (a) modification of the combination rules of the classical D-S evidence theory to achieve the redistribution of conflicts in the case of evidence conflicts. [33][34][35][36] Such measures alleviate the conflict between the bodies of evidence to a certain extent, but at the same time, it is difficult for the new rules to maintain the original properties of the D-S combination rules. (b) Keep the classic combination rules unchanged, and preprocess conflict data before combination. [37][38][39][40][41][42] Haenni 43 believes that the modification of the data model is in engineering, mathematics and philosophically more reasonable. However, regardless of the type of method used to deal with conflicts between evidence, the conflict coefficient is an extremely important parameter. Therefore, the most critical step before choosing the right combination of evidence is the measure of evidence conflict.To measure the conflict between evidence bodies, many researchers have proposed...

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Maintenance strategy selection with risky evaluations using RAHP

Cited by 35 publications

References 44 publications

A new soft likelihood function based on power ordered weighted average operator

A new soft likelihood function based on power ordered weighted average operator

Sustainable Urban Transport Development by Applying a Fuzzy-AHP Model: A Case Study from Mersin, Turkey

An association coefficient of a belief function and its application in a target recognition system

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