Multi-attribute utility

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

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). 

Classification based on multi-attribute utility model... 

The next step was to produce rank ordered lists of the sites based on their suitability for locating clothing production sites. Although we can refer the classification of the chosen sites to the experts by questionnaire, the multi-attribute utility (MAU) model, a traditional systematic model for scoring, is more suitable for dealing with this classification problem, which will be utilized to benchmark the relative performance of the proposed ANN classification decision model. The MAU model can be mathematically stated as follows ... 

ABSTRACT The paper presents a decision support system (DSS) for evaluation of risk pipeline risk. The system is able to support risk assessment and risk ranking of sections of natural gas pipelines. The DSS uses an architecture based on a data base, a model base and user interface. The model base has been built based on Multi-Attribute Utility Theory. The multi-attribute approach analysis risks in three dimensions of impact. These dimensions are human, financial and environmental impact. The way in which the model translates decisionmakers preferences into risk management decisions is highlighted. The paper presents the DSS, including some dialogue modules of the system based on real appUcations. 

The problem of risk analysis in pipelines, especially to be treated imder a focus of Decision Theory, Utility Theory and the Multi-Attribute Utility Theory (MAUT), requires a decision support system with a very much rich computational capacity in all its components. The Decision Support System for Risk Analysis in installation of pipelines imder a MAUT approach should, at the same time, have substantial databases and models that meet the needs of the mathematical modeling to the problem, and a structured dialogue component to perform an intense communication with the decision-maker. 

The risk analysis in pipeline installations, especially been tackled by a multi-attribute utility theory approach, claim a well-structured computational tools. The decision support system to risk analysis on pipelines installations under a MAUT approach... 

It can be divided in two parties the first involving the elicitation of one dimensional utility function to each risk dimension and the second to the determine the multi-attribute utility function. 

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). 

In order to estimate risks at each section considered, the multi-attribute utility function is combined with the probability density function. For each section of the pipeline established and for each scenario considered accidental, the loss has been defined by equation 2 ... 

Sanayei, A. Mousavi, S. E Abdi, M. R, Mohaghar, A. 2008. An integrated group decision-making process for supplier selection and order allocation using multi-attribute utility theory and linear programming. Journal of the Franklin Institute. 345 (7) 731-747. 

In this context the paper focuses on the estimation of manpower within roughly defined project s tasks. Two models are applied, a multi-attribute utility and a fuzzy control model. Afterwards those models are compared. The comparison is based on the model features general model approach, input values and generated results (Adam 1996). The discussion of the model features is addressed by means of a case study. 

For risk assessment, a DSS (Lopes et al, 2009) is used that incorporates a decision model proposed by Brito Almeida (2009). This DSS is a system designed to assess risk levels of each pipeline section with its characteristics, ranking all sections in a multidimensional hierarchy of risk, based on Multi Attribute Utility Theory (MAUT). 

Multi-Attribute Utility Theory- MAUT is used to aggregate preference and consequence values amongst multiple dimensions taking into account the decision maker s preferences and his behavior, when considering cases of imcertainty and a clear measure of risk. 

According to Brito Almeida (2009), the multiattribute utility function is additive, which implies the independence of preferences among the dimensions. Thus the multi-attribute utility function U (h,ej) is obtained based on one-dimensional utility functions U(h), U(e) and U(f), as described below ... 

Using procedures for elicitation through a process of a structured set of questions, the decision maker makes probabilistic choices of lotteries involving payoffs of the three dimensions (Brito Almeida, 2009). After this process, scale constants were obtained for the multi-attribute utility function, these scale constants are k = 0.45, hi = 0.15 and hi = 0.40. 

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

Bedford, T. 2005. Keynote lecture Multi-attribute utility theory and FN-criteria, In K. Kolowrocki (ed.) ESREL 2005 Advances in Safety and Reliability 157-166. Gdansk A.A. Balkema I blishers. 

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). 

Keywords, multi-modal transportation security risk analysis game-theory multi-attribute utility... 

Edwards, W., 1977. How to Use Multi Attribute Utility Measurement for Social Decision-making. IEEE Transactions on Systems, Man and Cybernetics, 7(5) 326-340. 

Multi-attribute utility theory, which hes within the concepts of MCDA, enables the values of the DM s preferences to be aggregated (through the utility function) from multiple attributes, together with the uncertainty inherent in their consequences E(c 0, q) and hazard scenarios 7i(0), in a unique synthesis criterion function. 

Another step of the model includes estimating the risks and ranking them as presented in Table 1. To estimate the risk assigned to each section of the pipeline, the multi-attribute utility function is associated with the probability density function of the consequences. 

The use of a multiple criteria approach to evaluate risk from a natural gas pipeline allows a multidimensional risk analysis to be conducted, as presented in this paper. More specifically, the use of MAUT (Multi-attribute Utility Theory) adds important aspects to this problem such as taking into consideration the decision makers preferences the decision makers behavior in relation to risk and using the interval scale for assessing the risk increment through increment ratios. 

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. 

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