The paper proposes a Multiple-Criteria Decision-Making (MCDM) methods-based approach to assess Learning Management Systems (LMS). The proposed approach includes the objective weighting method MEREC, used to determine the criteria weights, and CRADIS, applied in assessing alternatives and choosing the optimal one. It is revealed that the objectivity degree decreases when the qualitative type of criteria, which strongly depends on the subjective opinion of decision-makers, is used. The proposed approach gave adequate results, confirmed by conducting a sensitivity analysis based on the TOPSIS, ARAS, and MARCOS methods, and by comparing the results with similar research studies.
Drawing upon the choice models developed in the multiple criteria decision making (MCDM) area, this paper proposes an architecture for designing an intelligent decision support system (DSS) that is intended to aid in making choices among multiple alternatives along multiple dimensions. It argues that effective support can be provided to the decision maker when the knowledge‐based DSS is capable of dynamically selecting choice models appropriate to the domain and context of a particular problem being specified by the decision maker, and of properly applying them to the problem solution. Development of a prototype intended to partially represent application of the architecture is described. The paper concludes with suggestions for research extensions.
The objective of the book is to provide materials to demonstrate the development of TOPSIS and to serve as a handbook. It contains the basic process of TOPSIS, numerous variant processes, property explanations, theoretical developments, and illustrative examples with real-world cases. Possible readers would be graduate students, researchers, analysts, and professionals who are interested in TOPSIS, a distance-based algorithm, and who would like to compare TOPSIS with other MCDM methods. The book serves as a research reference as well as a self-learning book with step-by-step illustrations for the MCDM community.
The aim of the study is the application of multi-criteria evaluation methods for ranking of candidates in e-voting. Due to the potential to enhance the electoral efficiency in e-voting multiple criteria, such as personality traits, activity and reputation in social media, opinion followers on election area and so on for the selection of qualified personnel can be considered. In this case, the number of criteria excesses in the decision-making stage directed us to the use of a multi-criteria decision making model (MCDM). This paper proposes MCDM for weighted ranking of candidates in e-voting. Criteria for the candidates' ranking and selection are determined and each voter uses the linguistic scales for the ranking of each candidate. Candidates' ranking is evaluated according to all criteria. In a numerical study, it is provided the candidates' evaluation on the base of selected criteria and ranked according to the importance of criteria. To assess the importance of the criteria and to evaluate the suitability of the candidates for each of the criteria, the voters use linguistic variables. In practice, the proposed model can use different evaluation scales for the selection of candidates in e-voting. The proposed model allows selecting a candidate with the competencies based on the criteria set out in the e-voting process and making more effective decisions.
AbstractPriorities in multi-criteria decision-making (MCDM) convey the relevance preference of one criterion over another, which is usually reflected by imposing the non-negativity and unit-sum constraints. The processing of such priorities is different than other unconstrained data, but this point is often neglected by researchers, which results in fallacious statistical analysis. This article studies three prevalent fallacies in group MCDM along with solutions based on compositional data analysis to avoid misusing statistical operations. First, we use a compositional approach to aggregate the priorities of a group of DMs and show that the outcome of the compositional analysis is identical to the normalized geometric mean, meaning that the arithmetic mean should be avoided. Furthermore, a new aggregation method is developed, which is a robust surrogate for the geometric mean. We also discuss the errors in computing measures of dispersion, including standard deviation and distance functions. Discussing the fallacies in computing the standard deviation, we provide a probabilistic criteria ranking by developing proper Bayesian tests, where we calculate the extent to which a criterion is more important than another. Finally, we explain the errors in computing the distance between priorities, and a clustering algorithm is specially tailored based on proper distance metrics.
Tesis por compendio ; [EN] Academics, managerial and policy making community reinforce that renewable energy investments are one of the most effective instruments to attain CO2 emission reduction targets set by the Kyoto Protocol and by the recent Paris Agreement signed at the Paris climate conference (COP21) in December 2015 in which 195 countries adopted the first-ever universal, legally binding global climate deal. The problem of financing Renewable Energy (RE) projects has become a crucial issue for private and public decision makers worldwide. Budget constraints from governments and limited bank lending capacities have led to a reconsideration of the traditional financial instruments in the RE sector. The lack of credit makes impossible for commercial banks to fund RE projects with traditional loans. Research on new financing techniques for RE projects, such as Project Finance (PF) has gained interest in recent years. PF is a recent technique applied in large investments projects. During the last decades of the 20th century new public private partnership schemes enabled large infrastructure, energy and environmental projects. In these sectors PF has been used to reduce cost agency conflicts and better risk management. There is a wide number of contributions underlying the relevance of RE, however there is a lack of research on the financial aspects of RE projects. This research aims to make several contributions. First, to provide a better understanding of the PF technique and its use in the RE sector. Second, to fill the gap of research on financial aspects of RE in the literature by reviewing contributions of MCDM to RE project evaluation from the investor's perspective. Third, we propose a MPDM Moderate Pessimism Decision Making model, which adds to the rational financial evaluation of investment opportunities a set of non-financial factors that affects the investor's decisions. Finally, within the illustrative example, we apply this multi-criteria decision making process to help banks to decide if they ...
Decision-making is primarily a process that involves different actors: people, groups of people, institutions and the state. As a discipline, multi-criteria decision-making has a relatively short history. Since 1950s and 1960s, when foundations of modern multi-criteria decision-making methods have been laid, many researches devoted their time to development of new multi-criteria decision-making models and techniques. In the past decades, researches and development in the field have accelerated and seem to continue growing exponentially. Despite the intensive development worldwide, few attempts have been made to systematically present the theoretical bases and developments of multi-criteria decision-making methods. However, the methodological choices and framework for assessment of decisions are still under discussion. The article describes the situation with reviews of MCDM/MADM methods. Furthermore, there is a need for research to study the strengths and weaknesses of different decision-making methods.
Decision-making is primarily a process that involves different actors: people, groups of people, institutions and the state. As a discipline, multi-criteria decision-making has a relatively short history. Since 1950s and 1960s, when foundations of modern multi-criteria decision-making methods have been laid, many researches devoted their time to development of new multi-criteria decision-making models and techniques. In the past decades, researches and development in the field have accelerated and seem to continue growing exponentially. Despite the intensive development worldwide, few attempts have been made to systematically present the theoretical bases and developments of multi-criteria decision-making methods. However, the methodological choices and framework for assessment of decisions are still under discussion. The article describes the situation with reviews of MCDM/MADM methods. Furthermore, there is a need for research to study the strengths and weaknesses of different decision-making methods.
The study aims to rank the performance of Indian private sector banks on the basis of their financial capability. The study is focussed on those banks which are listed in the Bombay Stock Exchange (BSE), for the period from 2014–15 to 2018–19. The financial performance of selected Indian private banks is ranked using a hybrid Multi‐Criteria Decision Making (MCDM) technique, that is, AHP‐TOPSIS. The Analytical Hierarchy Process (AHP) is used to determine the weights of 10 financial performance pointers, and further Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to provide rankings and benchmarking of the private sector banks. Thereafter, overall ranking is given to the selected banks using interval‐valued TOPSIS (IV‐TOPSIS) for a combined period of 5 years. Sensitivity analysis has been performed to check the robustness of the method and its impact on the final ranking of the banks. Results showed that HDFC bank is the top performer and is ranked first for all the years consecutively during the period 2014–2019 and has set a financial indicator benchmark for other competitor banks. South Indian Bank showed the worst results in each year and stood last in the overall ranking. The IndusInd bank is followed after HDFC bank by improving its performance from 2014–19. With a slight margin, Kotak Mahindra Bank ranked after IndusInd. Through the AHP‐TOPSIS technique, each year the private sector banks ranking differs among themselves depending upon the financial performance pointers. Based on interval‐valued TOPSIS (IV‐TOPSIS), it was found that the overall HDFC was the top‐performing bank and South Indian Bank was the worst‐performing bank among all other banks. The results obtained from sensitivity analysis were found to be consistent with IV‐TOPSIS. The paper used the AHP technique, which is subjective in nature, and the results depend on the opinion of the experts. This study will assist the investors who want to channelize their funds in private banks having shares enlisted in BSE. It will accommodate banks to compare and judge their financial performance along with improving their performance and planning their future strategy. AHP‐TOPSIS utilized by past researchers is unable to bring out the cumulative performance for banks, but in this paper, IV‐TOPSIS is utilized, which can be effectively used to evaluate the cumulative performance of banks. The study tries to fill the gap in the literature by providing the overall ranking to private sector banks in India on the basis of financial indicators.
