INTEGRATED DESIGN OF ALTERNATIVE ENERGY OPTIONS - A MULTICRITERIA APPROACH

Energy planning has come a long way during the 20th century from an intuitive approach to a full-scale discipline, incorporating technological and economic dimensions. The latter include both the micro- and the macro- level, whereas the technological framework covers energy, technology, thermodynamics and thermo-economic approaches. It is only during the last two decades that the environmental aspects of energy conversion has started to assume the gravity that it should have been assigned perhaps from the start, with the deterioration of the environment, e.g. acid rain, urban pollution, global warming, etc. and the depletion of resources becoming issues of outmost importance. The emergence of the renewable energy technologies as a reliable substitute of conventional fossil fuels gave promises that were only partially fulfilled as they never assumed the role that society had entrusted on them in the beginning. The alternative energy options, both on the technological and the resource level, revealed the complex nature of energy planning, where energy production and conversion should be addressed in tandem with energy demand and consumption and the particular preferences of the individuals. In both cases the spatial elements should be carefully analyzed and taken into consideration. Today’s energy planning asks for a complex approach which must includes the technological, economic, environmental and social design, accounting for the multitude of facets that interweave in the analysis and successful implementation of energy policies and projects. The aforementioned four dimensions must in turn be decomposed in a number of attributes in order for a quantitative and qualitative estimation to be realized. For the identification of an appropriate solution, a multi-criteria analysis seems to be the logical framework since it allows for a multitude of elements to be incorporated, and at the same time it can include a variety of stakeholders, with conflicting perhaps interests. In this paper we present the new approach for energy planning with the technological, economic, environmental and social design dimensions integrated in a new platform together with the necessary decomposition analysis. The whole new framework is presented via theoretical and practical examples and will hopefully pave the way towards a new under transition, energy future.

Preference intensity in MCDM when an additive utility function represents DM preferences

We propose a new method for ranking alternatives in multicriteria decision-making problems when there is imprecision concerning the alternative performances, component utility functions and weights. We assume decision maker?s preferences are represented by an additive multiattribute utility function, in which weights can be modeled by independent normal variables, fuzzy numbers, value intervals or by an ordinal relation. The approaches are based on dominance measures or exploring the weight space in order to describe which ratings would make each alternative the preferred one. On the one hand, the approaches based on dominance measures compute the minimum utility difference among pairs of alternatives. Then, they compute a measure by which to rank the alternatives. On the other hand, the approaches based on exploring the weight space compute confidence factors describing the reliability of the analysis. These methods are compared using Monte Carlo simulation

Dealing with imprecise information in group multicriteria decisions: a methodology and a GDSS architecture

This paper outlines a distributed GDSS suitable to be used over the Internet, based on the VIP Analysis methodology and software. VIP Analysis incorporates complementary approaches to deal with the aggregation of multicriteria performances by means of an additive value function under imprecise information. This proposed GDSS intends to support a decision panel forming a democratic decision unit, whose members wish to reach a final decision in a choice problem, based on consensus or on some majority rule. Its purpose is not to impose an aggregated model from the individual ones. Rather, the GDSS is designed to reflect to each member the consequences of his/her inputs, confronting them with analogous reflections of the group members' inputs. We propose aggregation procedures to provide a reflection of the group's inputs to each of its members, and an architecture for a GDSS implementing these procedures.http://www.sciencedirect.com/science/article/B6VCT-4B5JR15-7/1/ec69930c867d6cea3058a818f9f33fe

A state-of-the-art multi-criteria model for drug benefit-risk analysis

Drug benefit-risk analysis is based on firm clinical evidence related to various safety and efficacy outcomes, such as tolerability, treatment response, and adverse events. In this paper, we propose a new approach for constructing a supporting multi-criteria model that fully takes into account this evidence. Our approach is based on the Stochastic Multicriteria Acceptability Analysis (SMAA) methodology, which allows us to compute the typical value judgments that support a decision, to quantify uncertainty, and to compute a comprehensive benefit-risk profile. As an example, we constructed a multi-criteria model for the therapeutic group of second-generation antidepressants. We analyzed Fluoxetine, Paroxetine, Sertraline, and Venlafaxine according to relative efficacy and absolute rates of several common adverse drug reactions using meta-analytical data from the literature. Our model showed that there are clear trade-offs among the four drugs. Based on our experiences from this study, SMAA appears to be a suitable approach for quantifying trade-offs and decision uncertainty in drug benefit-risk analysis.

Application of the stochastic multicriteria acceptability analysis for market share forecasting for new products

Wprowadzenie nowego produktu jest procesem kosztownym i długotrwałym. Brak informacji o preferencjach potencjalnych klientów stanowi zawsze duży problem w prognozowaniu przyszłych udziałów w rynku. W artykule proponowane jest nowatorskie wykorzystanie metody stochastycznej wielowymiarowej analizy akceptowalności (SMAA), do badania przewagi konkurencyjnej nowych produktów i prognozowania ich udziałów w rynku. Możliwe jest to dzięki reinterpretacji podstawowych wskaźników metody SMAA. Dzięki temu, posiadając jedynie informacje o udziałach w rynku i specyfikacji technicznej poszczególnych produktów, możliwa jest prognoza przyszłych udziałów w rynku nowo wprowadzanych produktów. W celu lepszego zobrazowania idei kryjącej się za proponowaną metodą został przedstawiony prosty przykład liczbowy. W przykładzie tym do modelowania procesu decyzyjnego wykorzystano metodę PROMETHEE.The introduction of a new product is a costly and lengthy process. The lack of information about the preferences of potential customers is always a big problem in forecasting future market shares. This article proposes the innovative use of Stochastic Multicriteria Acceptability Analysis (SMAA), to study new products competitivenes and forecast their market share, with very limited resources of information. By reinterpretation of acceptability index to forecast market share of new product there are needed only market shares and technical characterization of products which has been former present on the market. Simple numerical example was included for better presentation of idea hidden behind of our method. PROMETHEE method was used for decision process modeling

Using Multicriteria Decision Analysis to Support Research Priority Setting in Biomedical Translational Research Projects

Translational research is conducted to achieve a predefined set of economic or societal goals. As a result, investment decisions on where available resources have the highest potential in achieving these goals have to be made. In this paper, we first describe how multicriteria decision analysis can assist in defining the decision context and in ensuring that all relevant aspects of the decision problem are incorporated in the decision making process. We then present the results of a case study to support priority setting in a translational research consortium aimed at reducing the burden of disease of type 2 diabetes. During problem structuring, we identified four research alternatives (primary, secondary, tertiary microvascular, and tertiary macrovascular prevention) and a set of six decision criteria. Scoring of these alternatives against the criteria was done using a combination of expert judgement and previously published data. Lastly, decision analysis was performed using stochastic multicriteria acceptability analysis, which allows for the combined use of numerical and ordinal data. We found that the development of novel techniques applied in secondary prevention would be a poor investment of research funds. The ranking of the remaining alternatives was however strongly dependent on the decision maker’s preferences for certain criteria

Dominance measuring methods for the selection of cleaning services in a European underground transportation company

Dominance measuring methods are a recent approach for dealing with complex decisionmaking problems with imprecise, incomplete or partial information within multi-attribute value/utility theory. These methods compute pairwise dominance values and exploit the information included in the dominance matrix in different ways to derive measures of dominance intensity to rank the alternatives under consideration. We review dominance measuring methods proposed in the literature, describing how their possible drawbacks have been progressively overcome, and comparing their performance with other existing approaches, like surrogate weighting methods, the adaptation of classical decision rules to encompass an imprecise decision context, SMAA or Sarabando and Dias’ method. An example of the selection of cleaning services in a European underground transportation company is used to illustrate dominance measuring methods in a real complex decision-making problem