A survey on stochastic multicriteria acceptability analysis methods

Tervonen, Tommi; Figueira, José Rui

doi:10.1002/mcda.407

Cited by 179 publications

(134 citation statements)

References 30 publications

(9 reference statements)

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“…For example, inclusion of MAVT in the Monte Carlo process for assessing the distribution of overall values of alternatives taking into account the uncertainty of criterion values and weight coefficients (SMAA-2 method [29,30] ), and utilization of fuzzy criterion values and fuzzy weights for determination of (fuzzy) overall values of alternatives under consideration (FMAVT, see section 5.2.4).…”

Section: Mavt (Multi-attribute Value Theory)mentioning

confidence: 99%

“…Despite extensive use of the expected utility concept (in MAUT, and, indirectly and implicitly, using mean criterion values and weights in MAVT), its application is not the only approach within decision analysis, and other approaches which do not use expected utility methods have been developed and implemented [29,30] .…”

Section: Promaa (Probabilistic Multi-criteria Acceptability Analysis)mentioning

confidence: 99%

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Decerns: A Framework for Multi-Criteria Decision Analysis

Yatsalo¹,

Didenko²,

Gritsyuk³

et al. 2015

IJCIS

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A new framework, Decerns, for multicriteria decision analysis (MCDA) of a wide range of practical problems on risk management is introduced. Decerns framework contains a library of modules that are the basis for two scalable systems: DecernsMCDA for analysis of multicriteria problems, and DecernsSDSS for multicriteria analysis of spatial options. DecernsMCDA includes well known MCDA methods and original methods for uncertainty treatment based on probabilistic approaches and fuzzy numbers. These MCDA methods are described along with a case study on analysis of multicriteria location problem.

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Section: Mavt (Multi-attribute Value Theory)mentioning

confidence: 99%

Section: Promaa (Probabilistic Multi-criteria Acceptability Analysis)mentioning

confidence: 99%

Decerns: A Framework for Multi-Criteria Decision Analysis

Yatsalo¹,

Didenko²,

Gritsyuk³

et al. 2015

IJCIS

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“…The SM AA model [44] and its derivatives [45,46] explore the weight space by finding weight combinations which result in a given alternative achieving a particular rank . The proportion of the whole weight space volume defined by those combinations is a measure of support for that rank.…”

Section: Application To Multicriteria Problemsmentioning

confidence: 99%

IMP: A decision aid for multiattribute evaluation using imprecise weight estimates

Jessop¹

2014

Omega

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. (2014) 'IMP : a decision aid for multiattribute evaluation using imprecise weight estimates. ', Omega., Further information on publisher's website:http://dx.doi.org/10.1016/j.omega.2014.05.001Publisher's copyright statement: NOTICE: this is the author's version of a work that was accepted for publication in Omega. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be re ected in this document. Changes may have been made to this work since it was submitted for publication. A de nitive version was subsequently published in Omega, 49, 2014, 10.1016/j.omega.2014.05.001. Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details.

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“…Variably charged groups characteristically exhibit a probability distributions (uniform, normal, log-normal, etc.). Gaussian or uniform distributions are typically used (for more information about SMAA methods, see [34]). …”

Section: Criteriamentioning

confidence: 99%

Classifying Nanomaterial Risks Using Multi-Criteria Decision Analysis

Linkov¹,

Steevens²,

Chappell³

et al. 2009

Nanomaterials: Risks and Benefits

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Abstract. There is rapidly growing interest by regulatory agencies and stakeholders in the potential toxicity and other risks associated with nanomaterials throughout the different stages of the product life cycle (e.g., development, production, use and disposal). Risk assessment methods and tools developed and applied to chemical and biological material may not be readily adaptable for nanomaterials because of the current uncertainty in identifying the relevant physico-chemical and biological properties that adequately describe the materials. Such uncertainty is further driven by the substantial variations in the properties of the original material because of the variable manufacturing processes employed in nanomaterial production. To guide scientists and engineers in nanomaterial research and application as well as promote the safe use/handling of these materials, we propose a decision support system for classifying nanomaterials into different risk categories. The classification system is based on a set of performance metrics that measure both the toxicity and physico-chemical characteristics of the original materials, as well as the expected environmental impacts through the product life cycle. The stochastic

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A survey on stochastic multicriteria acceptability analysis methods

Cited by 179 publications

References 30 publications

Decerns: A Framework for Multi-Criteria Decision Analysis

Decerns: A Framework for Multi-Criteria Decision Analysis

IMP: A decision aid for multiattribute evaluation using imprecise weight estimates

Classifying Nanomaterial Risks Using Multi-Criteria Decision Analysis

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