Interpreting the Determinants of Sensitivity in MCDM Methods with a New Perspective: An Application on E-Scooter Selection with the PROBID Method (original) (raw)
Related papers
Stock selection using a hybrid MCDM approach
Croatian Operational Research Review, 2014
The problem of selecting the right stocks to invest in is of immense interest for investors on both emerging and developed capital markets. Moreover, an investor should take into account all available data regarding stocks on the particular market. This includes fundamental and stock market indicators. The decision making process includes several stocks to invest in and more than one criterion. Therefore, the task of selecting the stocks to invest in can be viewed as a multiple criteria decision making (MCDM) problem. Using several MCDM methods often leads to divergent rankings. The goal of this paper is to resolve these possible divergent results obtained from different MCDM methods using a hybrid MCDM approach based on Spearman's rank correlation coefficient. Five MCDM methods are selected: COPRAS, linear assignment, PROMETHEE, SAW and TOPSIS. The weights for all criteria are obtained by using the AHP method. Data for this study includes information on stock returns and traded volumes from March 2012 to March 2014 for 19 stocks on the Croatian capital market. It also includes the most important fundamental and stock market indicators for selected stocks. Rankings using five selected MCDM methods in the stock selection problem yield divergent results. However, after applying the proposed approach the final hybrid rankings are obtained. The results show that the worse stocks to invest in happen to be the same when the industry is taken into consideration or when not. However, when the industry is taken into account, the best stocks to invest in are slightly different, because some industries are more profitable than the others.
Mathematics, 2022
A major difficulty in comparing and even choosing MCDM methods is the uncertainty of information about the consistent and unique characteristics of the results produced. The objective information content of the final scores produced by MCDM methods and their relevance to real life can give us an important idea about them. In this study, first of all, seven MCDM methods with different methodologies were applied to evaluate companies’ financial performance. Then, the obtained MCDM scores were compared using two different objective verification mechanisms. The first validation criterion is the relationship of a MCDM method to real-life rankings (share price). The second criterion is the standard deviation (SD) technique used to discover the objective information content of MCDM final scores. According to the results of this study, PROMETHEE and FUCA definitely outperform other methods in terms of both SD values and strength of correlation with reference real-life rankings. Also, FUCA i...
Journal of intelligent management decision, 2024
In general, a stable and strong system shouldn't have an overly sensitive/dependent response to inputs (unless consciously and planned desired), as this would reduce efficiency. As in other techniques, approaches, and methodologies, if the results are excessively affected when the input parameters change in MCDM methods, this situation is identified with sensitivity analyses. Oversensitivity is generally accepted as a problem in the MCDM (Multi-Criteria Decision Making) methodology family, which has more than 200 members according to the current literature. The MCDM family is not just a weight coefficient-sensitive methodology. MCDM types can also be sensitive to many different calculation parameters such as data type, normalization, fundamental equation, threshold value, preference function, etc. Many studies to understand the degree of sensitivity simply monitor whether the ranking position of the best alternative changes. However, this is incomplete for understanding the nature of sensitivity, and more evidence is undoubtedly needed to gain insight into this matter. Observing the holistic change of all alternatives compared to a single alternative provides the researcher with more reliable and generalizing evidence, information, or assumptions about the degree of sensitivity of the system. In this study, we assigned a fixed reference point to measure sensitivity with a more robust approach. Thus, we took the distance to the fixed point as a base reference while observing the changeable MCDM results. We calculated sensitivity to normalization, not just sensitivity to weight coefficients. In addition, past MCDM studies accept existing data as the only criterion in sensitivity analysis and make generalizations easily. To show that the model proposed in this study is not a coincidence, in addition to the graphics card selection problem, an exploratory validation was performed for another problem with a different set of data, alternatives, and criteria. We comparatively measured sensitivity using the relationship between MCDM-based performance and the static reference point. We statistically measured the sensitivity with four types of weighting methods and 7 types of normalization techniques with the PROBID method. The striking result, confirmed by 56 different MCDM ranking findings, was this: In general, if the sensitivity of an MCDM method is high, the relationship of that MCDM method to a fixed reference point is low. On the other hand, if the sensitivity is low, a high correlation with the reference point is produced. In short, uncontrolled hypersensitivity disrupts not only the ranking but also external relations, as expected.
The International Journal of Advanced Manufacturing Technology, 2009
Multi criteria decision making (MCDM) models are widely used in selection problems in the literature. In the applications of MCDM approaches, selection criteria are directly taken and no specific tests are performed to determine their independency or check any other characteristics. Besides having independent criteria, it is especially important to limit the number of criteria to around seven to have a model which is sensitive to changes in the criteria weights. In this study, correlation test is used to obtain an independent set of criteria and reduce the number of criteria to a manageable level. It has been shown in the paper that the obtained set of independent criteria still fully represents the characteristics of the selection problem. Keywords Correlation test. Machine tool selection. Multi criteria decision making (MCDM). Spearman's rank correlation test. Technique for order preference by similarity to ideal solution (TOPSIS)
Sensitivity analysis in ranking and selection for multiple performance measures
Proceedings of the 31st …, 1999
In this paper, we conduct sensitivity analysis on a ranking and selection procedure for making multiple comparisons of systems that have multiple performance measures. The procedure combines multiple attribute utility theory with ranking and selection to select the best configuration from a set of K configurations using the indifference zone approach. Specifically, we consider sensitivity analysis on the weights generated by the multiple attribute utility assessment procedure. We demonstrate our analysis on a simulation model of a large project that has six performance measures.
Information
In this paper, a new multicriteria decision-making (MCDM) method, called a measure for information values connected to the equilibrium points (IVEP) method, and a new statistical measure for measuring the similarities of performances of MCDM algorithm outputs in a comparison process, called the Zakeri–Konstantas performance correlation coefficient, are introduced. The IVEP method uses Shannon’s entropy as the primary tool to measure the information embedded in the decision matrix in order to evaluate the decision’s options/alternatives for complex decision-making problems with a large number of criteria and alternatives. The second concept that drives the IVEP method is the equilibrium points, which signify the points in a vector space where scores for the decision’s options/alternatives are equilibrated. Instead of using linear functions to compute similarities between the data sets generated by the MCDM algorithms by the calculation of the distance using different methods, the Zak...
PeerJ Computer Science
When it comes to choosing the best option among multiple alternatives with criteria of different importance, it makes sense to use multi criteria decision making (MCDM) methods with more than 200 variations. However, because the algorithms of MCDM methods are different, they do not always produce the same best option or the same hierarchical ranking. At this point, it is important how and according to which MCDM methods will be compared, and the lack of an objective evaluation framework still continues. The mathematical robustness of the computational procedures, which are the inputs of MCDM methods, is of course important. But their output dimensions, such as their capacity to generate well-established real-life relationships and rank reversal (RR) performance, must also be taken into account. In this study, we propose for the first time two criteria that confirm each other. For this purpose, the financial performance (FP) of 140 listed manufacturing companies was calculated using ...
Preference intensity in MCDM when an additive utility function represents DM preferences
2012
We propose a new method for ranking alternatives in multicriteria decision-making problems when there is imprecision concerning the alternative performances, component utility functions and weights. We assume decision maker?s preferences are represented by an additive multiattribute utility function, in which weights can be modeled by independent normal variables, fuzzy numbers, value intervals or by an ordinal relation. The approaches are based on dominance measures or exploring the weight space in order to describe which ratings would make each alternative the preferred one. On the one hand, the approaches based on dominance measures compute the minimum utility difference among pairs of alternatives. Then, they compute a measure by which to rank the alternatives. On the other hand, the approaches based on exploring the weight space compute confidence factors describing the reliability of the analysis. These methods are compared using Monte Carlo simulation.
Measuring Congruence of Ranking Results Applying Particular MCDM Methods
Informatica
The aim of the current research is to measure objective congruence (incongruence) of the results obtained in a process of multiple criteria analysis when applying different MCDM methods. The methodology for evaluation of ranking results is developed on the ground of a case study of the redevelopment of derelict buildings as well as on composed experimental tasks. Fuzzified COPRAS, TOPSIS and VIKOR methods are applied for ranking the alternatives. Calculation results are evaluated by applying mathematical statistics methods. A methodology for measuring the congruence (incongruence) of the relative significances of alternatives is proposed.