A New Method of Multi-Criteria Analysis for Evaluation and Decision Making by Dominant Criterion

© 2019-Vilnius University. This paper introduces a new method for multi-criteria analyses where the failure to meet the dominant criterion of an alternative causes low values for the entire alternative. In this method, the introduction of new alternatives into the multi-criteria model does not affect the existing alternatives in the model. The new method was applied for the rating of ten websites of dental clinics in Serbia, which provide prosthetic services to tourists. The dominant criterion was the amount of information provided by the site

THE USE OF THE PIVOT PAIRWISE RELATIVE CRITERIA IMPORTANCE ASSESSMENT METHOD FOR DETERMINING THE WEIGHTS OF CRITERIA

The weights of evaluation criteria could have a significant impact on the results obtained by applying multiple criteria decision-making methods. Therefore, the two extensions of the SWARA method that can be used in cases when it is not easy, or even is impossible to reach a consensus on the expected importance of the evaluation criteria are proposed in this paper. The primary objective of the proposed extensions is to provide an understandable and easy-to-use approach to the collecting of respondents’ real attitudes towards the significance of evaluation criteria and to also provide an approach to the checking of the reliability of the data collected

WEBIRA - comparative analysis of weight balancing method

The attributes weight establishing problem is one of the most important MCDM tasks. This study summarizes weight determining approach which is called WEBIRA (WEight Balancing Indicator Ranks Accordance). This method requires to solve complicated optimization problem and its application is possible by carrying out non trivial calculations. The efficiency of WEBIRA and other MCDM methods – SAW (Simple Additive Weighting) and EMDCW (Entropy Method for Determining the Criterion Weight) compared for 4 different data normalization methods. The re-sults of the study revealed that more sophisticated WEBIRA method is significantly efficient for all considered numbers of alternatives. Efficiency of all methods decreases with increasing number of alternatives, but WEBIRA is still applicable, while appli-cation of other methods is impossible as the number of alternatives is greater than 11. WEBIRA is the least affected by the data normalization, while EMDCW is the most affected method

DEVELOPMENT OF A NEW HYBRID MULTI CRITERIA DECISION-MAKING METHOD FOR A CAR SELECTION SCENARIO

Increasing competition in the automobile industry has led to a vast variety of choices when buying a car thus making car selection a tedious task. The objective of this research is to develop a new hybrid multi-criteria decision-making technique, with accuracy greater than that of the already existing methods, in order to help the people in decision-making while buying a car. Hence, considering a broader spectrum, this study aims at easing the process of multi-criteria decision-making problems in different fields. To achieve the objective, seven different alternatives were evaluated with respect to the enlisted evaluation criteria, which were selected after analyzing the secondary data obtained from Pak wheels based on style, fuel economy, price, comfort and performance. These criteria were then analyzed using the proposed Full Consistency Fuzzy TOPSIS method. As the name tells, this method is a unique combination of two techniques. The Full Consistency method is used to calculate the weights of the criteria while the Fuzzy TOPSIS approach is applied to rank the alternatives according to their scores in the selected criteria. The outcomes demonstrate an increase in the consistency ratio of the weight coefficients due to which the ranking of the alternatives by the FCF-TOPSIS is more accurate than the TOPSIS and the Analytical Hierarchy Process. The novelty of the method lies in the fact that this combination has not been used for an alternative selection scenario before. In addition to this, it can be used in various industries where a choice between the available alternatives arises based on a set of evaluation criteria

Multi-Criteria Decision Analysis for Optimizing CO2 and NH3 Removal by Scenedesmus dimorphus Photobioreactors

Numerous technologies have been investigated for mitigating air pollutant emissions from swine barns. Among them, algal photobioreactors (PBRs) can remove and utilize air pollutants such as CO2 and NH3 from barn exhaust. However, a challenge to PBR operation is that it involves multiple system input parameters and output goals. A key question is then how to determine the appropriate CO2 and NH3 concentrations in this case. Conventional statistical methods are inadequate for handling this complex problem. Multi-criteria decision-making (MCDM) emerges as a practical methodology for comparison and can be utilized to rank different CO2–NH3 interactions based on their environmental and biological performance. By employing MCDM methods, producers can effectively control the ratio of CO2 and NH3 concentrations, enabling them to identify the optimal range of operating parameters for various housing types, ensuring efficient pollutant mitigation. In this study, a multi-criteria decision-making (MCDM) approach was employed to support operation management. Specifically, influent CO2 and NH3 concentrations were optimized for three scenarios (the best biological, environmental, and overall performance), using a combination of two MCDM techniques. This study is anticipated to facilitate the system analysis and optimization of algae-based phytoremediation processes

The recalculation of the weights of criteria in MCDM methods using the bayes approach

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

Improvement of business decision-making in the IT industry using the MCDM approach

The selection of suitable individuals for critical roles within the organization can significantly affect the business efficiency and performance of the organization. For this reason, this article presents a multiple-criteria decision-making procedure for candidates' assessment in the Information Technologies industry (IT) using the integrated PIPRECIA-S and WS-PLP methods. The introduced approach involved defining the criteria' significance with the help of the PIPRECIA-S, while the WS-PLP method was used to evaluate candidates and harmonize the views of decision-makers attitudes. The applicability of the suggested technique was reviewed in the situation of selecting an IT Project Manager in an IT company. However, it can easily be adapted for similar cases of candidate selection

Multiple Criteria Evaluation and the Inverse Hierarchy Model for Justifying the Choice of Rail Transport Mode

The choice of a particular mode of transport as an alternative to another one is subjective and usually based on an individual passenger’s approach to the evaluation of advantages and disadvantages of some particular means of transport. The paper presents the methods of analysing the reasons for passengers’ choice of travelling by train as an alternative to using air transport and the results obtained in the research. The 16 criteria (sub-criteria), describing the advantages of travelling by rail over air travel, are defined. The data of the survey questionnaire filled by 52 passengers of the Vilnius–Moscow train and the ranks assigned by them to the considered criteria are described. The average ranks of all 16 criteria and their normalized subjective weights are calculated by using a new method of average rank transformation into weight (ARTIW). The average ranks assigned by the passengers of the train to sub-criteria and the calculated global weights show what criteria are most important. Using the inverse hierarchy model based on the sub-criteria weights, the most and the least important groups of criteria are determined. The institutions and companies engaged in passenger transportation by rail, which give priority to improving the services described by the most important criteria, can make this mode of transport more attractive to people.</p

Assessing the environmental competitiveness of cities based on a novel MCDM approach

Many factors affect the competitiveness of cities. One of the most important of these factors is the environmental dimension, which can affect and be influenced by economic and sociocultural aspects of urban competitiveness. The present study assesses the environmental competitiveness of cities with populations of more than 500,000 in Iran. Our research weighting approach consists of integrated ITARA FUCOM methods to obtain nine criteria weights based on actual data evaluation and expert ideas. In addition, experts' statements are presented using gray logic and transformed into crisp numbers. Then, a modified MARCOS method that uses logarithmic normalization is introduced and implemented to assess fourteen target cities. Finally, The results of MARCOS-LN are compared to those of MARCOS itself, as well as three more MCDM methods (EDAS, CODAS, TOPSIS) and their versions, which utilize logarithmic normalization. The research findings showed that the city of Rasht is the most environmentally competitive, while the city of Kerman is the least competitive (rank 14) among the Iranian cities with populations greater than 500,000. The research results indicate that to improve the competitive position of Iranian cities, the internal capacities, relative advantages, and the competitive role each city can have on a transnational scale, their internal capacities should be paid attention to. This requires decentralized national and transnational planning and development competitiveness scenarios for medium and long-term periods

Development of a Novel Integrated CCSD-ITARA-MARCOS Decision-Making Approach for Stackers Selection in a Logistics System

The main goal of this paper is to propose a Multiple-Criteria Decision-Making (MCDM) approach that will facilitate decision-making in the field of logistics—i.e., in the selection of the optimal equipment for performing a logistics activity. For defining the objective weights of the criteria, the correlation coefficient and the standard deviation (CCSD method) are applied. Furthermore, for determining the semi-objective weights of the considered criteria, the indifference threshold-based attribute ratio analysis method (ITARA) is used. In this way, by combining these two methods, the weights of the criteria are determined with a higher degree of reliability. For the final ranking of the alternatives, the measurement of alternatives and ranking according to the compromise solution method (MARCOS) is utilized. For demonstrating the applicability of the proposed approach, an illustrative case study pointing to the selection of the best manual stacker for a small warehouse is performed. The final results are compared with the ones obtained using the other proved MCDM methods that confirmed the reliability and stability of the proposed approach. The proposed integrated approach shows itself as a suitable technique for applying in the process of logistics equipment selection, because it defines the most influential criteria and the optimal choice with regard to all of them in a relatively easy and comprehensive way. Additionally, conceiving the determination of the criteria with the combination of objective and semi-objective methods enables defining the objective weights concerning the attitudes of the involved decision-makers, which finally leads to more reliable result