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This paper considers the technique to construct the general decision rule for the contradictory expert classification of objects which are described with many qualitative attributes. This approach is based on the theory of multiset metric spaces, and allows to classify a collection of multi-attribute objects and define the classification rule which approximates the set of individual sorting rules.

This paper considers some aspects of using a cascade-correlation network in the investment task in which it is required to determine the most suitable project to invest money. This task is one of the most often met economical tasks. In various bibliographical sources on economics there are described different methods of choosing investment projects. However, they all use either one or a few criteria, i.e., out of the set of criteria there are chosen most valuable ones. With this, a lot of information contained in other choice criteria is omitted. A neural network enables one to avoid information losses. It accumulates information and helps to gain better results when choosing an investment project in comparison with classical methods. The cascade-correlation network architecture that is used in this paper has been developed by Scott E. Fahlman and Cristian Lebiere at Carnegie Mellon University.

The new method for the construction of partial order on the set of multicriteria alternatives is presented. This method belongs to the family of Verbal Decision Analysis methods and gives a more efficient means of problem solution. The method is based on psychologically valid operations for information elicitation from a decision maker: comparisons of two distances between the evaluations on the ordinal scales of two criteria. The information received from a decision maker is used for the construction of a binary relation between a pair of alternatives which yields preference, indifference and incomparability relations. The notion of a method decisive power is introduced. The illustrative example is given.

Multicriteria sequencing problems with criteria ordered according to their importance are considered. Additional precedence and group technology constraints are imposed. We introduce a notion of a priority-generating vector function and suggest general techniques that form a base for the construction of polynomial time algorithms for numerous sequencing problems including all known polynomially solvable problems. A comprehensive survey of results for sequencing problems with ordered criteria is given as well.

The analysis of the method for multiple criteria optimization problems applying a computer network has been presented in the paper. The essence of the proposed method is the distribution of the concrete optimization problem into the network rather than the parallelization of some optimization method. The aim of the authors is to design and investigate the interactive strategies to solve complex multiple criteria problems by applying a computer network. The optimized objective function is the weight sum of the criteria. The multiple criteria problem is iterated by selecting interactively different weight coefficients of the criteria. Therefore, the process is organized by designating the computers as the master (that coordinates the process of other computers) and the slaves (that execute different tasks). In the beginning of the process the researcher allocates a certain number of optimization problems to the network. The objective function optimization problems differ only in weight coefficients of the criteria. As soon as the task of a slave has been executed, the result is sent to the master. Every computer of the network behaves in analogous way. Whenever the researcher receives an immediate result from one of the computers, he gives a decision taking into consideration the latter and all the previous results, i.e., he selects new weight coefficients for the criteria and assigns a new task to the network. Likewise the multiple criteria problem is solved until the result is acceptable for the researcher. The application of the proposed method is illustrated on the basis of the problem for the selection of the optimal nutritive value. Message Passing Interface (MPI) software has been used. The trials have been carried out with the network of computers under the operation system Windows NT.

A rich georeferenced data base on a flood event in the north west of Italy and the knowledge of experts from the different involved disciplines have been used as the basis for the application of an outranking method, ELECTRE III, oriented to the structuring and validation of consistent multicriteria models for hazard evaluation. The models were developed with the aim of explaining the multidimensional nature of slope instability and erosion phenomena in the study area and to help in the definition of a hazard map using criteria which synthesize different interpretations of these phenomena. The integrated use of multicriteria modeling and data analysis, in a GIS analysis context, resulted in a deeper insight into these natural phenomena and could be the proposal of a more ‘flexible’ information system oriented towards decision aiding.

The PC software AMCE is proposing building actors (owner, project authors$\dots$) a Multiple Criteria Decision Aid procedure (MCDA) to optimise the building envelope about cost and energy performances, during the sketch design.

In this work the analytical expressions of half-time T1/2 and its derivatives in respect to distance ∂T1/2/∂ R in a one-dimensional RC medium (a current electrode has a shape of the segment) and in a two-dimensional RC medium (a current electrode has a shape of the circle) were received. First, by using a well-known in electrostatics a superposition principle, the current's electrodes were divided into elementary point sources by positioning them on the perimeter or the surface of the electrode. Second, with the help of the computer-simulation, the dependencies of T1/2 and ∂T1/2/∂ R on the current electrode dimensions, the degree of electrotonic anisotropy, and the distance between the current electrode and the potential measurement place were calculated. Our calculations demonstrate that the slope of the function T1/2=f(R) depends both on the distance between the potential measurement place and the current electrode, as well as the measurement direction in respect to the fibers' direction. Furthermore, the slope value can be less or greater to 0.5.

The result of simulation of an idealized thin wet film connecting fixed points in the Euclidean plane is a length-minimizing curve. Gradually increasing the exterior pressure we are able to achieve the film configuration near to the Steiner minimal tree. This film evolution may be an interesting tool for solving the Euclidean Steiner problem, but several dead-point situations may occur for a certain location of fixed points. A continuous evolution of the film is impossible by increasing the pressure in these situations. The investigation of dead-point situations gives the ways of overcoming the difficulties of dead-point situations and continuing the film evolution by temporarily decreasing pressure.

Methods for solving stochastic optimization problems by Monte-Carlo simulation are considered. The stoping and accuracy of the solutions is treated in a statistical manner, testing the hypothesis of optimality according to statistical criteria. A rule for adjusting the Monte-Carlo sample size is introduced to ensure the convergence and to find the solution of the stochastic optimization problem from acceptable volume of Monte-Carlo trials. The examples of application of the developed method to importance sampling and the Weber location problem are also considered.