Multi-attribute methods

Success Likelihood Index Method/Multi-Attribute Utility Decomposition (SUM-MAUD)... 

The objective weights quantify the trade-off relationship between the sub-objectives. They can be obtained with any of the above-mentioned methods, but commonly a direct estimation is used with these simple methods. The combination of the simple scoring method with the ratio or swing weights approach is also referred to as Simple Multi-Attribute Rating Technique or SMART (cf. Goodwin and Wright 2004, pp. 27-58 von Winterfeldt and Edwards 1986, pp. 259-287). 

Multi-attribute utility models Analytic network process Data envelopment analysis In product development, the data that is available for decision making is often imprecise and fuzzy. MCDM models, however, often cannot effectively support decision making based on such imprecise information. To resolve this difficulty, fuzzy MADM methods are developed (Rao 2007). Fuzzy logic is a branch of mathematics that allows a computer to model the real world the same way that people do. It provides a simple way to reason with vague, ambiguous, and imprecise input or knowledge (Kahraman 2008). 

The main method for modelling preferences under uncertainty is the Multi-Attribute Utility Theory (MAUT). In its simples (additive) form, a multi attribute utility function resembles a multi-attribute value function. The way to find parameters of a utility function is however different. While in the case of MAVT the scores and weights can be determined based on direct comparison of consequences, in the case of MAUT these components are found through lottery types of questions (Keeney Raiffa, 1999). 

MAUT measures complete preferences rmder rmcertainty. However, because preferences may not always be completely specified (internal uncertainty), methods have been developed to deal with value intervals, qualitative estimations and incom-pletes in judgements. Examples of such methods are PRIME (Preference Ratios In Multi-attribute Evaluation) (Sale Hamalainemi, 2001) and ER (Evidential Reasoning) approach (Yang Xu, 2002), among others. 

Edwards, W. Barton, EH. 1994. Smarts and smarter improved simple methods for multi attribute utility measurement. Organizational Behavior and Human Decision Processes. 60 306U125. 

Kuesten, C., Bi, J. and Eeng, Y. (2013) Exploring taffy product consumption experiences using a multi-attribute time-intensity (MATI) method, Food Quality and Preference, 30, 260-273. 

In order to make a eomparative analysis, a weighting factor method was established based on experts opinions through interviews and surveys. The simple multi-attribute rating teehnique (SMART) was used as a tool to arrive at a decision. The main stages in the SMART analysis are summarized in the following paragraph. 

SLIM Success likelihood index method. It is an HRA quantification technique by which HEPs are quantified. For taking actions, this may be utilized in conjunction with multi-attribute utility decomposition (MAUD), discussed later. Here, SLI (Ref Clause 6.2.2) is calibrated. It should actually be considered under expert judgment type. It has wider application as it is somewhat generic. 

SLIM-MAUD SLIM (as well as FLIM) method requires expert judgment and when they are used with an interactive computer program called multi-attribute utility decomposition (MAUD). It is called SLIM-MAUD. 

Most MCDA methodologies, including Multi-Attribute Utility Theory (MAUT), Analytical Hierarchy Process (AHP), and outranking, share similar steps (Steps 1 and 3), but diverge on their approach to Steps 2 and 4. A detailed analysis of the theoretical foundations of different MCDA methods and their comparative strengths and weaknesses is presented in Belton and Steward (2002). 

In addition to the multi-criteria representation of the problem, relationships between projects, companies, and products should be taken into account. In such assessments, premium cost and maximal insured value can be found using Multi-Objective Decision-Making (MODM) methods and solving as a multi-criteria optimization problem (Figure 5.2) the same criteria can be reused for insurance portfolio optimization, and in the case of discrete alternatives (premium cost or insured value), the Multi-Attribute Decision-Making (MADM) approach can be used. Different risk assessment approaches can be adapted for MCDM for example, product lifecycle can be presented in detail and/or insured accidents can be presented implicitly along with business opportunities and other benefits. 

In particular, Partial Least Square Regression (PLS) and multi-way method, such as N-PLS, were applied to study the relationships between the volatile fractions, sampled and characterised by using head space solid phase micro extraction (HS-SPME)/GC techniques, and the sensory attributes obtained by expert panellists [65,66]. 

Decision analysis originates in the field of operations research but has links to economics, mathematics, psychology and human factors. A wide range of tools have been developed which utilise a variety of methods such as influence diagrams, decision trees, voting methods, multi-attribute utility methods and so on. 

Currie, K. R. and Creese, R. C. Justification of Cellular Manufacturing Using Multi-Attribute Part Family Loading - MAPFLO. Justification Methods for Computer Integrated Manufacturing Systems. Elsevier Press (1990) 203-219. 

There are numerous approaches to the problem of capturing all the information in a set of multi endpoint data. When the data are continuous in nature, approaches such as the analog plot can be used (Chemoff, 1973 Chambers et al., 1983 Schmid, 1983). A form of control chart also can be derived for such uses when detecting effect rather than exploring relationships between variables is the goal. When the data are discontinuous, other forms of analysis must be used. Just as the control chart can be adapted to analyzing attribute data, an analog plot can be adapted. Other methods are also available. 

Validation is the determination of the attributes, or figures of merit, of an analytical method for one or more analytes in one or more sample matrices by one or more analysts in one or more analytical laboratories and the acceptance of the attributes as reasonable and useful by the users of the data. There are many levels of analytical method validation ranging from the validation of a method for a single analyte in a single matrix by a single analyst in a single laboratory to a multi-analyte, multi-matrix, multi-analyst, and multi-laboratory validation. 

Yuen et al. 2008 (43) MWCNT CVD CNT Company, Incheon, Korea As-received In situ method Ex situ method CNT Loading levels 0.25 to 4.76 wt% Film Bulk Film Bulk Electrical Conductivity Percolation threshold 0.25 wt% for in situ prepared composites Percolation threshold 0.74 wt% for ex situ prepared composites EMI SE (2-18 GHz) 32.06 dB at 2.18 GHz for bulk composite prepared by in situ method 58.73 dB at 14.84 GHz for 10 layers of stacked composite film prepared by in situ method Higher EMI shielding effectiveness of layered composite attributed to energy loss by multi-reflections in the layered composites... 

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