Dominance Measuring Method Performance under Incomplete Information about Weights.

In multi-attribute utility theory, it is often not easy to elicit precise values for the scaling weights representing the relative importance of criteria. A very widespread approach is to gather incomplete information. A recent approach for dealing with such situations is to use information about each alternative?s intensity of dominance, known as dominance measuring methods. Different dominancemeasuring methods have been proposed, and simulation studies have been carried out to compare these methods with each other and with other approaches but only when ordinal information about weights is available. In this paper, we useMonte Carlo simulation techniques to analyse the performance of and adapt such methods to deal with weight intervals, weights fitting independent normal probability distributions orweights represented by fuzzy numbers.Moreover, dominance measuringmethod performance is also compared with a widely used methodology dealing with incomplete information on weights, the stochastic multicriteria acceptability analysis (SMAA). SMAA is based on exploring the weight space to describe the evaluations that would make each alternative the preferred one

Application of fuzzy TRUST CRADIS method for selection of sustainable suppliers in agribusiness

This study deals with the selection of a sustainable supplier on the example of the agribusiness company Mamex from Bosnia and Herzegovina. The main problem of this research is the selection of a sustainable supplier as a part of the sustainable strategy of the Mamex company. One of the prerequisites is that suppliers must present sustainability principles in business by having an appropriate certificate. The results of the selection of sustainable suppliers are completed using a new hybrid fuzzy approach with the methods IMF SWARA (Improved Fuzzy Stepwise Weight Assessment Ratio Analysis) and fuzzy TRUST (multi-normalization multi-distance assessment) CRADIS (compromise ranking of alternatives from distance to ideal solution) methods. The innovative approach is reflected in the use of a combination of these methods, especially by combining the TRUST and CRADIS methods into one method. The IMF SWARA method shows that the most important main criterion is the economic criterion, while the least important is the social criterion. By applying the fuzzy TRUST CRADIS method, it is found that out of the observed six suppliers, the second supplier has the best indicators. These results are confirmed by other fuzzy methods: MABAC (multi-attributive border approximation area comparison), WASPAS (weighted aggregated sum product assessment), fuzzy SAW (simple additive weighting), MARCOS (measurement of alternatives and ranking according to compromise solution), ARAS (a new additive ratio assessment), and TOPSIS (technique for order preference by similarity to an ideal solution). This research shows that applying more normalization when ranking alternatives reduces the influence of individual normalizations, and this approach should be used in future research

Ranking Alternatives on the Basis of the Intensity of Dominance and Fuzzy Logic within MAUT

We introduce dominance measuring methods to derive a ranking of alternatives to deal with incomplete information in multi-criteria decision making problems on the basis of Multi-Attribute Utility Theory (MAUT). We consider the situation where the alternative performances are represented by uniformly distributed intervals, and there exists imprecision concerning the decision-makers¿ preferences, by means of classes of individual utility functions and imprecise weights represented by weight intervals or fuzzy weights, respectively. An additive multi-attribute utility model is used to evaluate the alternatives under consideration, which is considered a valid approach in most practical cases. The approaches we propose are based on the dominance values between pairs of alternatives that can be computed by linear programming, which are then transformed into dominance intensities from which a dominance intensity measure is derived. The methods proposed are compared with other existing dominance measuring methods and other methodologies by Monte Carlo simulation techniques. The performance is analyzed in terms of two measures of efficacy: hit ratio, the proportion of all cases in which the method selects the same best alternative as in the TRUE ranking, and the Rank-order correlation, which represents how similar the overall rank structures of alternatives are in the TRUE ranking and in the ranking derived from the method. The approaches are illustrated with an example consisting on the selection of intervention strategies to restore an aquatic ecosystem contaminated by radionuclides

GIS-based multicriteria analysis as decision support in flood risk management

In this report we develop a GIS-based multicriteria flood risk assessment and mapping approach. This approach has the ability a) to consider also flood risks which are not measured in monetary terms, b) to show the spatial distribution of these multiple risks and c) to deal with uncertainties in criteria values and to show their influence on the overall assessment. It can furthermore be used to show the spatial distribution of the effects of risk reduction measures. The approach is tested for a pilot study at the River Mulde in Saxony, Germany. Therefore, a GISdataset of economic as well as social and environmental risk criteria is built up. Two multicriteria decision rules, a disjunctive approach and an additive weighting approach are used to come to an overall assessment and mapping of flood risk in the area. Both the risk calculation and mapping of single criteria as well as the multicriteria analysis are supported by a software tool (FloodCalc) which was developed for this task. --

SIMPLE ADDITIVE WEIGHTING FOR MODELING DSS TO DETERMINE THE BEST COLLEGE IN PRINGSEWU

Continuing study to a college is a hope for prospective students. However, that is an easy problem, because that needs to be considered in choosing such as, the cost during the lecture took place, facilities, facilities and infrastructure, cooperation between universities and others. By using The Decision Support System, the calculation of all criteria can be able to determine the best college to assist prospective students in selecting and choosding the college. The Decision Support System uses the Simple Additive Weighting (SAW) method. The Decision Support Issues are basically for the selection of alternative actions enabling prospective students in choosing the college which they want, and will produce a best decision for prospective students for continuing in the best collage

A METHODOLOGY FOR THE BIDDERS EVALUATION AND SELECTION IN THE PUBLIC PROCUREMENT PROCESS BASED ON HETEROGENEOUS INFORMATION AND ADAPTIVE CONSENSUS APPROACHES

The public procurement problem is a special problem of supplier selection that requires strict adherence to the principles of non-discrimination, free competition, and transparency in the contract awarding procedures. It is a very complex multi-criteria problem, which requires the engagement of several decision-makers (experts). The public procurement problem requires the usage of different types of conflicting criteria, the combination of different models (methods and techniques) of decision-making, as well as the modeling of different forms of uncertainty, inaccuracy, and subjectivity of decision-makers, which can represent a rather complex, difficult, and lengthy decision-making process. Therefore, the paper proposes a methodology for improving the tender process that focuses on heterogeneous preference structures of information (preference ordering, utility values, fuzzy (additive) preference relations, multiplicative preference relations, and linguistic preference relations) and an adaptive consensus approach for subjectively determining the weight of criteria and evaluation and selection of alternative bids. The Simple Additive Weighting (SAW) method is used for the final ranking of bidders. The proposed methodology enables obtaining a more objective and measurable value during subjective decision-making as well as minimizing the risk of unscrupulous, incompetent, and irresponsible decision-making, which is shown in the given example

A weighted rough set based fuzzy axiomatic design approach for the selection of AM processes

Additive manufacturing (AM) or 3D printing, as an enabling technology for mass customization or personalization, has been developed rapidly in recent years. Various design tools, materials, machines and service bureaus can be found in the market. Clearly, the choices are abundant, but users can be easily confused as to which AM process they should use. This paper first reviews the existing multi-attribute decision-making methods for AM process selection and assesses their suitability with regard to two aspects, preference rating flexibility and performance evaluation objectivity. We propose that an approach that is capable of handling incomplete attribute information and objective assessment within inherent data has advantages over other approaches. Based on this proposition, this paper proposes a weighted preference graph method for personalized preference evaluation and a rough set based fuzzy axiomatic design approach for performance evaluation and the selection of appropriate AM processes. An example based on the previous research work of AM machine selection is given to validate its robustness for the priori articulation of AM process selection decision support