Ranking efficient decision-making units in data envelopment analysis using fuzzy concept

Preston

Research in Transportation Business & Management

2016

Stations are bottlenecks for railway transportation as they are where traffics merge and diverge. Numerous activities such as passengers boarding, alighting and interchanging, train formation and technical checks are also done at these points. The number of platforms is limited and it is vital to do all the work efficiently. For the first time in the literature, we implement a methodology based on data envelopment analysis which is benchmarked from ports and airport efficiency studies. It can help policy makers and practitioners to rank stations in terms of efficiency and take more informative decisions. The proposed methodology can analyse the relative 'technical efficiency' of stations to handle train stops with existing station capacity. The second stage model analyses 'service effectiveness' to identify how well train stops at a station are transformed into the number of passenger entries, exits and interchanges, taking into account catchment area population and job opportunities. The models are applied to a case study of the 96 busiest train stations in Great Britain and are followed up by two Tobit regressions to assess the effect of traffic type and location on the results.

Section: Data Envelopment Analysis (Dea)mentioning

confidence: 99%

Evaluating efficiency of passenger railway stations: A DEA approach

Preston

Research in Transportation Business & Management

2016

“…The proposed method for ranking completely all efficient and inefficient, DMUs consists of four stages. Majid et al [24] In Firstly using DEA to evaluate the efficient and inefficient decision-making units (DMUs) with the CCR model. Secondly the resulted weights for each output are considered as fuzzy sets and are then converted to fuzzy number.…”

Section: Fuzzy Multi-objective Linear Programming (Fmolp)mentioning

confidence: 99%

“…Data Envelopment Analysis (DEA) is a powerful tool for assessing the performance of organizations and their functional units Majid et al [24]. DEA spans the boundaries of several academic areas and receive increasing importance as a tool for evaluating and improving the performance of manufacturing and service operations.DEA is a nonparametric technique for measuring the relative efficiencies of a set of organizations with which consume multiple-inputs to produce multiple-outputs.…”

Section: Introductionmentioning

confidence: 99%

Ranking Decision making units using Fuzzy Multi-Objective Approach

Zaher¹,

ZeinEldin²,

Abdelshafy³

2015

IJCA

This paper presents Data Envelopment Analysis (DEA) has been used in a wide variety of applied research and it is a linear programming methodology that has been widely used to evaluate the performance of a set of decision-making units (DMUs). It requires crisp input and output data. However, in reality input and output cannot be measured in a precise manner. Firstly using DEA to evaluate the efficient and inefficient decision-making units (DMUs) with the (CCR) model. Secondly the resulted weights for each input and output are considered as fuzzy sets and are then converted to fuzzy number. Thirdly using Fuzzy multi-objective approach to find the highest and lowest of the weighted values. Fourthly usingthe results from stage it to rank from highest to lowest. An application from banking industry is presented.

Data Envelopment Analysis and Decision Science

“…Data envelopment analysis (DEA) developed by Charnes et al, is a methodology for measuring the best relative efficiencies of a group of peer decision-making units (DMUs) that consume multiple inputs to produce multiple outputs with the most favorable input and output weights to each of the DMUs, respectively, and has found significant applications in production economics [13,14]. Also [15], have introduced DEA, as a non-parametric technique for measuring the relative efficiencies of a set of decisionmaking units (DMUs) which consume multiple-inputs to produce multiple-outputs. Charnes et al, introduced the ratio definition of efficiency, also known as the CCR ratio definition, which generalizes the single-output to single-input ratio definition used in classical science to multiple outputs and inputs without requiring pre-assigned weights [16]:…”

Section: Data Envelopment Analysismentioning

confidence: 99%

Classification of Iran Automotive and Parts Manufacturing stocks by DEA

Afshar¹,

Hooshangi²

2014

One of the most important subjects of DEA area is efficiency analysis and classification of firms. It is important that stock holders know all information about stocks. Efficiency of stock market plays a significant role in transfer of invests The most important goal of the present paper is to evaluate the efficiency of automotive and parts manufacturing branch in Tehran Stock Exchange (TSE), to select a group of desirable securities from a list of 31. We applied data envelopment analysis for ranking and evaluating the units. First, the efficiency of units presented in three different types: technical, management and scale efficiency, and inefficient units were identified. So, peer groups, their weights and grade of units introduced. Finally, optimal amounts of inputs calculated for some of inefficient units. The Results show that the units (7, 8, 14, 18, 19, 20, 21, 23, 24, 25 and 29) are known as superior units. Unit 7 obtained the first rank of 29 units because this DMU get the efficient rank in three types and was introduced 5 as the peer unit for the others so called the stronger unit. 19 of the firms are classified in efficient scale.The units 3,10, 11, 13, 16 & 17 have surplus inputs, it means that these units have used more inputs rather than the other units which are in the same level and suggested reduce the surplus amount for achieving the same efficiency.