Revisiting the interval and fuzzy topsis methods: Is euclidean distance a suitable tool to measure the differences between fuzzy numbers?

Euclidean distance (ED) calculates the distance between n-coordinate points that n equals the dimension of the space these points are located. Some studies extended its application to measure the difference between fuzzy numbers (FNs).This study shows that this extension is not logical because although an n-coordinate point and an FN are denoted the same, they are conceptually different. An FN is defined by n components; however, n is not equal to the dimension of the space where the FN is located. This study illustrates this misapplication and shows that the ED between FNs does not necessarily reflect their difference. We also revisit triangular and trapezoidal fuzzy TOPSIS methods to avoid this misapplication. For this purpose, we first defuzzify the FNs using the center of gravity (COG) method and then apply the ED to measure the difference between crisp values. We use an example to illustrate that the existing fuzzy TOPSIS methods assign inaccurate weights to alternatives and may even rank them incorrectly

The equity theory: A quantitative perspective using data envelopment analysis

Equity theory (ET) is an organizational theory investigating how fairly people feel they have been treated. The literature on ET does not address two essential questions: what is the magnitude of the equity that one may perceive compared to other members in an organization?, and how much should be the resources (outcomes) of an underpaid member reduced (increased) to feel equal? The group members may respond to these questions emotionally, and their answers could be biased based on their personalities. This paper proposes a novel method using data envelopment analysis (DEA) to quantify the ET and answer these questions more logically. DEA is a mathematical model that is conceptually similar to ET. We will show how DEA can estimate the degree of equity perceived by members of a group with different personalities, including optimistic, pessimistic, benevolent, and entitled characters

Hotel Performance in the UK:The Role of Information Entropy in a Novel Slack-Based Data Envelopment Analysis

Previous hotel performance studies neglected the role of information entropy in feedback processes between input and output management. This paper focuses on this gap by exploring the relationship between hotel performance at the industry level and the capability of learning by doing and adopting best practices using a sample of 153 UK hotels over a 10-year period between 2008–2017. Besides, this research also fills a literature gap by addressing the issues of measuring hotel performance in light of negative outputs. In order to achieve this, we apply a novel Modified slack-based model for the efficiency analysis and Least Absolute Shrinkage and Selection Operator to examine the influence of entropy related variable on efficiency score. The Results indicate that less can be learnt from inputs than from outputs to improve efficiency levels and resource allocation is more balanced than cash flow and liquidity. The findings suggest that market dynamics explains the cash flow generation potential and liquidity. We find that market conditions are increasingly offering the opportunities for learning and improving hotel efficiency. The results report that the distinctive characteristic of superior performance in hotel operations is the capability to match the cash flow generation potential with market opportunities

General and multiplicative non-parametric models with interval ratio data: application to the banking industry

The empirical literature has tried to propose relevant data envelopment analysis (DEA) models to evaluate the efficiency level of the decision-making unit (DMU) in the presence of interval ratio data; however, the use of variable production frontier in the evaluation suffers from a number of limitations. The current study fills in the gap in the previous literature by proposing relevant DEA models based on interval arithmetic, through which the shortcomings of the previous existing studies have been overcome. The findings show that extra variable changes are not needed by the proposed model and a fixed, unified production frontier can be used to measure the DMUs’ efficiency with interval data. The potential application of the proposed model is illustrated through a numerical example in the banking industry

A linear programming technique to solve fuzzy multiple criteria decision making problems with an application

Generally, in real world multiple criteria decision making (MCDM) problems, we concern with inaccurate data. This paper transforms a fuzzy multiple criteria decision making (FMCDM) problem into two linear programming models based on simple additive weighting method (SAW). The new linear models calculate two scores for each alternative in optimistic and pessimistic viewpoints. To rank the alternatives, the numerical value of the arithmetic mean of good score and bad score is used as final score of each alternative. Finally, we illustrate the practical applications of the proposed method in selection an industrial zone for construct dairy products factory

Air Pollution Assessment in China: A Novel Group Multiple Criteria Decision Making Model under Uncertain Information

Assessment of and controlling air pollution are urgent global issues where international cooperation is deemed necessary. Although a very relevant data source can be obtained through continuous monitoring of air quality, measuring air pollutant concentrations is quite difficult when compared to other environmental indicators. We mainly have three different aims for the current study: (1) we propose the computation of the interval weights of decision makers (DMs) based on a group multiple criteria decision making (GMCDM) model; (2) we aim to rank the overall preferences of DMs by the possibility concepts; (3) we aim to evaluate the air quality in China using the most recent data based on our proposed method. We consider three monitoring stations, namely Luhu Park, Wanqingsha, and Tianhu, and the data for SO2, NO2, and PM10 are collected for November 2017, 2018, and 2019. The results from our innovative model show that November 2019 had the best air quality. Finally, robustness analyses are also performed to confirm the discriminatory power of the proposed approach

Endogenous Performance of Rail Sections in Brazil: A Novel Two-Dimensional Fuzzy-Monte Carlo Approach

This paper investigates the endogenous source of railway performance in Brazil by focusing on several rail sections. A novel Two-Dimensional Fuzzy-Monte Carlo Analysis (2DFMC) approach is proposed including a multi-attribute decision-making (MADM) model based on type-2 fuzzy sets (T2FS) and a Stochastic Structural Relationship Programming (SSRP) Model based on neural networks. Results suggest that the level of inclination of the rail operators to use the bottlenecked rail sections influences the measurement of idleness, in a chained effect. It is suggested that the balance and coordination among bottleneck, minimum curve radius, and installed capacity are prerequisites to improve railway performance