Informatica

Multi-attribute analysis is a useful tool in many economical, managerial, constructional, etc. problems. The accuracy of performance measures in COPRAS (The multi-attribute COmplex PRoportional ASsessment of alternatives) method is usually assumed to be accurate. This method assumes direct and proportional dependence of the weight and utility degree of investigated versions on a system of attributes adequately describing the alternatives and on values and weights of the attributes. However, there is usually some uncertainty involved in all multi-attribute model inputs. The objective of this research is to demonstrate how simulation can be used to reflect fuzzy inputs, which allows more complete interpretation of model results. A case study is used to demonstrate the concept of general contractor choice of on the basis of multiple attributes of efficiency with fuzzy inputs applying COPRAS-G method. The research has concluded that the COPRAS-G method is appropriate to use.

Multi-criteria decision making is used in many areas of human activities. Each alternative in multi-criteria decision making problem can be described by a set of criteria. Criteria can be qualitative and quantitative. They usually have different units of measurement and different optimization direction. The normalization aims at obtaining comparable scales of criteria values. The normalization of criteria values is not always needed, but it may be essential. In the new program LEVI 3.1 the following normalization methods are possible: vector, linear scale, non-linear and new logarithmic techniques. Logarithmic normalization has never been used before. The present research is focused on introducing a new logarithmic method for decision making matrix normalization.

The paper analyses the problem of ranking accuracy in multiple criteria decision-making (MCDM) methods. The methodology for measuring the accuracy of determining the relative significance of alternatives as a function of the criteria values is developed. An algorithm of the Technique for the Order Preference by Similarity to Ideal Solution (TOPSIS) that applies criteria values' transformation through a normalization of vectors and the linear transformation is considered. A computational experiment is presented, to compare the results of a multiple criteria analysis and the ranking accuracy in a particular situation.

This paper considers the main positions of one‐sided and two‐sided problems. For one‐sided problems only the method of solution “the distance to the ideal point” is discussed in the actual version. For two‐sided problems a distinction is made between games with rational behaviour and games against nature. The main strategic principles are as follows: simple min‐max principle, extended min‐max principle, Wald's rule, Savage criterion, Hurwicz's rule, Laplace's rule, Bayes's rule, Hodges‐Lehmann rule. Questions of transforming the decision‐making matrix are considered. The article gives the description of a software as well as an example of an investment variant estimation.

A lot of data had to be processed and evaluated in carrying out multivariant design and multiple criteria analysis of a building life cycle. The number of feasible alternatives can be as large as 100 000. Each of the alternatives may be described from various perspectives (economic, technical, qualitative, technological, social, legislative, infrastructural, etc.). The problem arises how to perform multivariant design and multiple criteria analysis of the alternative variants based on this enormous amount of information. To solve this problem the methods of multivariant design and multiple criteria building life cycle analysis were developed. In order to demonstrate the theory an example is given in this paper.