A Hybrid Model Based on Fuzzy AHP and Fuzzy WASPAS for Construction Site Selection

Turskis, Zenonas; Zavadskas, Edmundas Kazimieras; Antuchevičienė, Jurgita; Kosareva, Natalja

doi:10.15837/ijccc.2015.6.2078

Cited by 229 publications

(113 citation statements)

References 34 publications

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“…

{\overset{true~}{r}}_{i} = {(\prod_{j = 1}^{n} {\overset{a}{true}}_{italicij})}^{1 / n}, i = 1, 2, \dots, n

Phase 4: Calculate the fuzzy priority of each criterion and subcriterion

((), {\overset{w}{true}}_{i})

by applying Equation .

{\overset{true~}{w}}_{i} = {\overset{true~}{r}}_{i} ⨂ {({\overset{r}{true}}_{1} \oplus {\overset{r}{true}}_{2} \oplus \dots \oplus {\overset{r}{true}}_{n})}^{- 1} = ((),,, l w_{i}, m w_{i}, u w_{i})

Phase 5: Defuzzify

{\overset{true~}{w}}_{i}

using the Centre of Area method (Turskis, Zavadskas, Antucheviciene, & Kosareva, ; Vahidnia, Alesheikh, & Alimohammadi, ) described in Equation , where M i represents a nonfuzzy number.

M_{i} = \frac{l w_{i} + m w_{i} + u w_{i}}{3}

Phase 6: Normalize M i values by applying Equation .…”

Section: Methodsmentioning

confidence: 99%

Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework

Ortíz‐Barrios

Nugent

Cleland

et al. 2019

Multi Criteria Decision Anal

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The number of people with dementia (PwD) is increasing dramatically. PwD exhibit impairments of reasoning, memory, and thought that require some form of self‐management intervention to support the completion of everyday activities while maintaining a level of independence. To address this need, efforts have been directed to the development of assistive technology solutions, which may provide an opportunity to alleviate the burden faced by the PwD and their carers. Nevertheless, uptake of such solutions has been limited. It is therefore necessary to use classifiers to discriminate between adopters and nonadopters of these technologies in order to avoid cost overruns and potential negative effects on quality of life. As multiple classification algorithms have been developed, choosing the most suitable classifier has become a critical step in technology adoption. To select the most appropriate classifier, a set of criteria from various domains need to be taken into account by decision makers. In addition, it is crucial to define the most appropriate multicriteria decision‐making approach for the modelling of technology adoption. Considering the above‐mentioned aspects, this paper presents the integration of a five‐phase methodology based on the Fuzzy Analytic Hierarchy Process and the Technique for Order of Preference by Similarity to Ideal Solution to determine the most suitable classifier for supporting assistive technology adoption studies. Fuzzy Analytic Hierarchy Process is used to determine the relative weights of criteria and subcriteria under uncertainty and Technique for Order of Preference by Similarity to Ideal Solution is applied to rank the classifier alternatives. A case study considering a mobile‐based self‐management and reminding solution for PwD is described to validate the proposed approach. The results revealed that the best classifier was k‐nearest‐neighbour with a closeness coefficient of 0.804, and the most important criterion when selecting classifiers is scalability. The paper also discusses the strengths and weaknesses of each algorithm that should be addressed in future research.

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“…

{\overset{true~}{r}}_{i} = {(\prod_{j = 1}^{n} {\overset{a}{true}}_{italicij})}^{1 / n}, i = 1, 2, \dots, n

Phase 4: Calculate the fuzzy priority of each criterion and subcriterion

((), {\overset{w}{true}}_{i})

by applying Equation .

{\overset{true~}{w}}_{i} = {\overset{true~}{r}}_{i} ⨂ {({\overset{r}{true}}_{1} \oplus {\overset{r}{true}}_{2} \oplus \dots \oplus {\overset{r}{true}}_{n})}^{- 1} = ((),,, l w_{i}, m w_{i}, u w_{i})

Phase 5: Defuzzify

{\overset{true~}{w}}_{i}

using the Centre of Area method (Turskis, Zavadskas, Antucheviciene, & Kosareva, ; Vahidnia, Alesheikh, & Alimohammadi, ) described in Equation , where M i represents a nonfuzzy number.

M_{i} = \frac{l w_{i} + m w_{i} + u w_{i}}{3}

Phase 6: Normalize M i values by applying Equation .…”

Section: Methodsmentioning

confidence: 99%

Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework

Ortíz‐Barrios

Nugent

Cleland

et al. 2019

Multi Criteria Decision Anal

View full text Add to dashboard Cite

show abstract

“…Turskis et al [50] Fuzzy AHP, Fuzzy WASPAS Selection the best construction site for shopping center project in Vilnius, Lithuania.…”

Section: Jiang Et Al [39] Ahpmentioning

confidence: 99%

A Hybrid MCDM Approach for Strategic Project Portfolio Selection of Agro By-Products

Debnath¹,

Roy

Kar

et al. 2017

Sustainability

Self Cite

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Due to the increasing size of the population, society faces several challenges for sustainable and adequate agricultural production, quality, distribution, and food safety in the strategic project portfolio selection (SPPS). The initial adaptation of strategic portfolio management of genetically modified (GM) Agro by-products (Ab-Ps) is a huge challenge in terms of processing the agro food product supply-chain practices in an environmentally nonthreatening way. As a solution to the challenges, the socio-economic characteristics for SPPS of GM food purchasing scenarios are studied. Evaluation and selection of the GM agro portfolio management are the dynamic issues due to physical and immaterial criteria involving a hybrid multiple criteria decision making (MCDM) approach, combining modified grey Decision-Making Trial and Evaluation Laboratory (DEMATEL), Multi-Attributive Border Approximation area Comparison (MABAC) and sensitivity analysis. Evaluation criteria are grouped into social, differential and beneficial clusters, and the modified DEMATEL procedure is used to derive the criteria weights. The MABAC method is applied to rank the strategic project portfolios according to the aggregated preferences of decision makers (DMs). The usefulness of the proposed research framework is validated with a case study. The GM by-products are found to be the best portfolio. Moreover, this framework can unify the policies of agro technological improvement, corporate social responsibility (CSR) and agro export promotion.

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“…The combination of the analytical hierarchical process (AHP) and WASPAS methods is not rare, so a number of publications using AHP for determining the weight values of the criteria and WASPAS for the choice of alternatives can be found in the literature [14,15]. Madić et al [16] evaluate the machining process with combination of AHP and WASPAS method, while Turskis et al [17] use the fuzzy form of these methods for construction site selection. A combination of the classic form of these methods is applied to laser cutting in [18].…”

Section: Applications Of Weighted Aggregated Sum Product Assessment (mentioning

confidence: 99%

A Novel Rough WASPAS Approach for Supplier Selection in a Company Manufacturing PVC Carpentry Products

et al. 2018

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Abstract:The decision-making process requires the prior definition and fulfillment of certain factors, especially when it comes to complex areas such as supply chain management. One of the most important items in the initial phase of the supply chain, which strongly influences its further flow, is to decide on the most favorable supplier. In this paper a selection of suppliers in a company producing polyvinyl chloride (PVC) carpentry was made based on the new approach developed in the field of multi-criteria decision making (MCDM). The relative values of the weight coefficients of the criteria are calculated using the rough analytical hierarchical process (AHP) method. The evaluation and ranking of suppliers is carried out using the new rough weighted aggregated sum product assessment (WASPAS) method. In order to determine the stability of the model and the ability to apply the developed rough WASPAS approach, the paper analyzes its sensitivity, which involves changing the value of the coefficient λ in the first part. The second part of the sensitivity analysis relates to the application of different multi-criteria decision-making methods in combination with rough numbers that have been developed in the very recent past. The model presented in the paper is solved by using the following methods: rough Simple Additive Weighting (SAW), rough Evaluation based on Distancefrom Average Solution (EDAS), rough MultiAttributive Border Approximation area Comparison (MABAC), rough Višekriterijumsko kompromisno rangiranje (VIKOR), rough MultiAttributiveIdeal-Real Comparative Analysis (MAIRCA) and rough Multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA). In addition, in the third part of the sensitivity analysis, the Spearman correlation coefficient (SCC) of the ranks obtained was calculated which confirms the applicability of all the proposed approaches. The proposed rough model allows the evaluation of alternatives despite the imprecision and lack of quantitative information in the information-management process.

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A Hybrid Model Based on Fuzzy AHP and Fuzzy WASPAS for Construction Site Selection

Cited by 229 publications

References 34 publications

Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework

Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework

A Hybrid MCDM Approach for Strategic Project Portfolio Selection of Agro By-Products

A Novel Rough WASPAS Approach for Supplier Selection in a Company Manufacturing PVC Carpentry Products

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