The identification of multi-criteria model of the significance of drainage pumping stations in Poland

Sałabun, Wojciech; Napierała, Michał; Bykowski, J.

doi:10.15576/asp.fc/2015.14.3.147

Cited by 11 publications

(5 citation statements)

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“…The fuzzy set theory was developed by L. Zadeh, who introduced the idea of fuzzy sets in [26]. The growing importance of the Fuzzy Set Theory in model creation in numerous scientific fields has proven to be an effective way to approach and solve multi-criteria decision problems [4], [27]- [29]. The necessary concepts of the Fuzzy Set Theory are recalled as follows [9], [30]- [32]:…”

Section: Fuzzy Set Theory: Preliminariesmentioning

confidence: 99%

Swimmer Assessment Model (SWAM): Expert System Supporting Sport Potential Measurement

2022

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The potential of a swimmer can be exploited to the full if an individual approach is applied to training early enough. In the initial stage of a competitor's career, it's hard to say whether the person will reach the world level. It is influenced by factors related to physical and mental characteristics, swimming technique or the current skills level of the athlete. Some of them cannot be improved by swimmers; however, a few of these factors can be affected and upgraded by working on them. The data from 30 swimmers were collected in the research. Then, adopting the COMET method, the values of attributes characteristic for the athletes were entered into the proposed model to determine the predispositions of each of them. The applied method proved to be effective in assessing the parameters of competitors, and at the same time, it is a modern approach free of the rank reversal phenomenon. In addition, the model obtained allows for manipulation of input data to check how the change of a given criterion will affect the overall rating. The model developed is forward-looking. The results are satisfactory but can be improved to analyse somatic traits better and achieve more accurate predictions.

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Section: Fuzzy Set Theory: Preliminariesmentioning

confidence: 99%

Swimmer Assessment Model (SWAM): Expert System Supporting Sport Potential Measurement

2022

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“…The idea of Fuzzy Set Theory was introduced by Lofti Zadeh in [55]. Fuzzy Set Theory is used in many scientific fields and could be especially useful for solving MCDA problems [56][57][58]. Here we present some definitions and basic concepts of the Fuzzy Set Theory which are necessary to understand COMET method [59][60][61].…”

Section: Fuzzy Set Theorymentioning

confidence: 99%

Efficiency of Methods for Determining the Relevance of Criteria in Sustainable Transport Problems: A Comparative Case Study

et al. 2020

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Problems related to sustainable urban transport have gained in importance with the rapid growth of urban agglomerations. There is, therefore, a need to support decision-making processes in this area, a trend that is visible in the literature. Many methods have already been presented as a useful decision-making tool in this field. However, it is still a significant challenge to properly determine the relevance of the criteria because it is one of the most critical points of many presented techniques to solve decision problems. In this work, we propose two new approaches to determining the relevance of particular decision criteria effectively in sustainable transport problems. For this purpose, we examine a study case for the evaluation of electric bikes evaluated against eight criteria, which have been taken from earlier work. We calculate the relevance of each criterion using four different approaches and then evaluate their effectiveness using a reference ranking and popular multi-criteria decision analysis methods. The results are compared with each other by using similarity coefficients. Finally, we summarize the results obtained and set out further methods of development.

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“…The idea of Fuzzy Set Theory was introduced by Lofti Zadeh in [42]. Fuzzy Set Theory is used in many scientific fields and could be especially useful for solving MCDA problems [43][44][45].…”

Section: Fuzzy Set Theorymentioning

confidence: 99%

Fuzzy Model Identification Using Monolithic and Structured Approaches in Decision Problems with Partially Incomplete Data

2020

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A significant challenge in the current trend in decision-making methods is the problem’s class in which the decision-maker makes decisions based on partially incomplete data. Classic methods of multicriteria decision analysis are used to analyze alternatives described by using numerical values. At the same time, fuzzy set modifications are usually used to include uncertain data in the decision-making process. However, data incompleteness is something else. In this paper, we show two approaches to identify fuzzy models with partially incomplete data. The monolithic approach assumes creating one model that requires many queries to the expert. In the structured approach, the problem is decomposed into several interrelated models. The main aim of the work is to compare their accuracy empirically and to determine the sensitivity of the obtained model to the used criteria. For this purpose, a study case will be presented. In order to compare the proposed approaches and analyze the significance of the decision criteria, we use two ranking similarity coefficients, i.e., symmetric rw and asymmetric WS. In this work, the limitations of each approach are presented, and the results show great similarity despite the use of two structurally different approaches. Finally, we show an example of calculations performed for alternatives with partially incomplete data.

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The identification of multi-criteria model of the significance of drainage pumping stations in Poland

Cited by 11 publications

References 11 publications

Swimmer Assessment Model (SWAM): Expert System Supporting Sport Potential Measurement

Swimmer Assessment Model (SWAM): Expert System Supporting Sport Potential Measurement

Efficiency of Methods for Determining the Relevance of Criteria in Sustainable Transport Problems: A Comparative Case Study

Fuzzy Model Identification Using Monolithic and Structured Approaches in Decision Problems with Partially Incomplete Data

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