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In this paper, one of the multi-criteria models in making decision, a Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), is described. Some of the advantages of TOPSIS methods are: simplicity, rationality, comprehensibility, good computational efficiency and ability to measure the relative performance for each alternative in a simple mathematical form. The paper has a review character. It systematises the knowledge within the scope of techniques of decision taking with the use of the TOPSIS method. Simple numerical examples that reference real situations show practical applications of different aspects of this method. The paper is organized as follows. The Introduction presents a short overview of the decision making steps as well as MCDM techniques. Section 1 presents matrix representation of the MCDM problem. Section 2 describes the TOPSIS procedure for crisp data, and Section 3 for interval data. The TOPSIS algorithm in group decision environment in the case of crisp and interval data is also presented. In Section 4 the problem of qualitative data in TOPSIS model is discussed. The numerical examples showing applications of those techniques in the negotiation process are presented in Section 5. Finally, conclusions and some concluding remarks are made in last section. [ABSTRACT FROM AUTHOR]

The aim of this paper is to apply TOPSIS method for negotiation support. The support we focus on concerns the pre-negotiation preparation and the process of negotiation template evaluation, which results in building a scoring system for the negotiation offers. Since the negotiation template may contain different types of criteria (negotiation issues), both quantitative (price, time) and qualitative (verbal description of warranty), the mechanisms of measuring distances for different types of data need to be incorporated into TOPSIS scoring procedure. We will use GDM (generalized distance measure) for interval and ordinal data. For weakly structured negotiation templates an alternative approach is proposed, one that does not use pair-wise comparisons of the evaluated alternatives. To illustrate the performance of TOPSIS in negotiation support we present a numerical example of business negotiations. [ABSTRACT FROM AUTHOR]

In project development it is hardly possible to get exhaustive and accurate information. As a result, the situations occur, the consequences of which can be very damaging to the project. Inaccurate evaluation of the strategy related to capital investment and project implementation is one of the reasons why such estimates are not required in practice. Instead, a classification approach may be used for this purpose. Classification is a very important aspect of decision making. In the present paper, a novel algorithm CLARA is offered for ordering multicriteria alternatives. It differs from the existing similar methods in the wider range of application allowing it to be used with various scales of criteria evaluation, a random number of the solution classes, incomplete order on the criteria scales as well as in the considerably rarefied space of the alternatives. The suggested algorithm is more effective in terms of the time spent by an expert. A comparative calculation of the efficiency of the algorithms used in classifying the objects in the order of their significance has shown that CLARA is much more effective than CLANSH and other algorithms. At the same time, its general effectiveness was found to be lower than that of the algorithms CYCLE which has a narrower scope of application. [ABSTRACT FROM AUTHOR]