The application of multiple criteria decision-making methods (MCDM) is aimed at choosing the best alternative out of the number of available versions in the absence of the apparently dominant alternative. One of the two major components of multiple criteria decision-making methods is represented by the weights of the criteria describing the considered process. The weights of the criteria quantitatively express their significance and influence on the evaluation result. The criterion weights can be subjective, i.e., based on the estimates assigned by the experts, and the so-called objective, i.e., those which assess the structure of the data array at the time of evaluation. Several groups of experts, representing the opinions of various interested parties may take part in the evaluation of criteria. The evaluation data on the criterion weights also depend on the mathematical methods used for calculations and the estimation scales. In determining the objective weights, several methods, assessing various properties or characteristics of the data array's structure, are usually employed. Therefore, the use of the procedures, improving the accuracy of the evaluation of the weights' values and the integration of the obtained data into a single value, is often required. The present paper offers a new approach to more accurate evaluation of the criteria weights obtained by using various methods based on the idea of the Bayes hypothesis. The performed investigation shows that the suggested method is symmetrical and does not depend on the fact whether a priori or posterior values of the weights are recalculated. This result is the theoretical basis for practical use of the method of combining the weights obtained by various approaches as the geometric mean of various estimates. The ideas suggested by the authors have been repeatedly used in the investigation for combining the objective weights, for recalculating the criteria weights after obtaining the estimates of other groups of experts and for combining the subjective and the objective weights. The recalculated values of the weights of the criteria are used in the work for evaluating the quality of the distant courses taught to the students. Keywords: MCDM; the criteria of the weights; Bayes' theorem; combining the weights; symmetry of the method; IDOCRIW; FAHP; evaluating the quality of distant courses

The goal of this paper is to compare quality assurance in different contractor contracts by means of multi-attribute decision-making (MADM) and to select the best option. For this investigation, the authors have developed the complex of quality evaluation criteria. During experimental evaluation, the significance of criteria was determined and the expert evaluation of template construction contracts was performed. The complex comparison of contractor contracts was carried out by means of the following MADM methods: Simple Additive Weighting (SAW), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Complex Proportional Assessment (COPRAS) as well as the new Evaluation Based on Distance from Average Solution (EDAS). MADM. method. To determine the weights of criteria, with due consideration of uncertainty of expert evaluation, the Fuzzy Analytic Hierarchy Process (FAHP) method was applied. Evaluation of the data structure was performed by methods for the determination of objective weights: an entropy method and new criteria impact loss (CILOS) and integrated determination of objective criteria weights (IDOCRIW) methods. Expert subjective and objective weights were combined into aggregate weights. Based on the investigation performed, the authors make conclusions regarding possibilities for improving quality assurance in contractor contracts.

Optimization problems are relevant to various areas of human activity. In different cases, the problems are solved by applying appropriate optimization methods. A range of optimization problems has resulted in a number of different methods and algorithms for reaching solutions. One of the problems deals with the decision-making area, which is an optimal option selected from several options of comparison. Multi-Attribute Decision-Making (MADM) methods are widely applied for making the optimal solution, selecting a single option or ranking choices from the most to the least appropriate. This paper is aimed at providing MADM methods as a component of mathematics-based optimization. The theoretical part of the paper presents evaluation criteria of methods as the objective functions. To illustrate the idea, some of the most frequently used methods in practice—Simple Additive Weighting (SAW), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Complex Proportional Assessment Method (COPRAS), Multi-Objective Optimization by Ratio Analysis (MOORA) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE)—were chosen. These methods use a finite number of explicitly given alternatives. The research literature does not propose the best or most appropriate MADM method for dealing with a specific task. Thus, several techniques are frequently applied in parallel to make the right decision. Each method differs in the data processing, and therefore the results of MADM methods are obtained on different scales. The practical part of this paper demonstrates how to combine the results of several applied methods into a single value. This paper proposes a new approach for evaluating that involves merging the results of all applied MADM methods into a single value, taking into account the suitability of the methods for the task to be solved. Taken as a basis is the fact that if a method is more stable to a minor data change, the greater importance (weight) it has for the merged result. This paper proposes an algorithm for determining the stability of MADM methods by applying the statistical simulation method using a sequence of random numbers from the given distribution. This paper shows the different approaches to normalizing the results of MADM methods. For arranging negative values and making the scales of the results of the methods equal, Weitendorf’s linear normalization and classical and author-proposed transformation techniques have been illustrated in this paper.

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The paper aims to present a new methodology to evaluate the quality of features and functionality of learning object repositories (LORs). The quality of features and functionality of LORs is analysed in terms of engaging LOR users and content producers. Thus, it can be referred to as quality-in-use of LORs. This methodology consists of creation and consequent application of methods and the model for the quality-in-use of LORs. The model of the quality-in-use of LORs is presented in this paper. The methodology for evaluating the quality-in-use of LORs is based on the general MCEQLS (Multiple Criteria Evaluation of the Quality of Learning Software) approach to evaluate the quality of learning software. The essential part of the novel methodology is the application of improved Fuzzy AHP method to establish criteria weights of the quality-in-use of LORs. It is shown that the created methodology is suitable and stable for evaluating the quality of LOR features and its functionality. A more detail presentation is given on the results of the expert evaluation of the quality-in-use of three LORs that are most popular in Lithuania against the proposed methodology. The novelty of the presented research is achieved through the innovative instrument consisting of the model of the quality-in-use of LORs and the Fuzzy AHP method. The presented methodology could serve as a technological tool for decision making in education as well as in different areas of economy.

This paper analyses and presents the new scientific models and methods for the expert evaluation of quality of learning objects (LOs) paying special attention to LOs reusability level. Currently all existing approaches in the area are quite subjective and depend only on the experience of the decision-makers. The authors analyse several scientific methods and principles to minimise the subjectivity level in the expert evaluation of LOs quality. They are: (a) the principles of multi-criteria decision analysis for identification of quality criteria, (b) technological quality criteria classification principle, (c) fuzzy group decision making theory to obtain evaluation measures, (d) normalisation of the weights of criteria, and (e) scalarisation method for LOs quality optimisation. The authors demonstrate that the complex application of these approaches could significantly improve the quality of the expert evaluation of LOs and noticeably reduce the level of the expert evaluation subjectivity. The paper also presents the example of practical application of these approaches for evaluation of LOs for Mathematics subject.

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In recent years, the focus of marketing methods has been the user rather than advertising. A wide range of customer satisfaction surveys, post-screening answers and reviews are a staple of marketing for that purpose. In search for innovative ways to survey customer satisfaction, new concepts have emerged recently and several biometric methods and systems have been developed. This research integrates Damasio's Somatic marker hypothesis, statistical analysis, biometric systems, the neuro-questionnaire, multiple criteria analysis methods and intelligent systems. The objective of this research was to develop the .INVAR Neuromarketing method and system. The INVAR Neuromarketing method and system can determine: the effectiveness of a video ad and its individual frames; ad frames that make viewers most happy, sad, angry, surprised, scared, disgusted, bored, interested or confused; the effect of a video ad on the short-term and long-term memory; the most positive or negative video ad, etc. This article presents these studies and their results in greater detail.