scholarly journals A Review of Data Envelopment Analysis in Airline Efficiency: State of the Art and Prospects

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Qiang Cui ◽  
Li-Ting Yu
Keyword(s):  
Data Envelopment Analysis ◽  
Rapid Development ◽  
Aviation Industry ◽  
Future Research ◽  
Data Envelopment ◽  
Social Concern ◽  
Network Dea ◽  
Advantages And Disadvantages ◽  
Dea Models ◽  
Future Research Directions

The rapid development of the aviation industry has brought about the deterioration of the climate, which makes airline efficiency become a hot issue of social concern. As an important nonparametric method, Data Envelopment Analysis (DEA), has been widely applied in efficiency evaluation. This paper examines 130 papers published in the period of 1993–2020 to summarize the literature involving the special application of DEA models in airline efficiency. The paper begins with an overall review of the existing literature, and then the radial DEA, nonradial DEA, network DEA, dynamic DEA, and DEA models with undesirable outputs applied in airline efficiency are introduced. The main advantages and disadvantages of the above models are summarized, and the drivers of airline efficiency are analyzed. Finally, the literature review ends up with future research directions and conclusions.

Measuring the Efficiency of Free and Open Source Software Projects Using Data Envelopment Analysis

2007 ◽  
pp. 25-46
Author(s):  
Stefan Koch
Keyword(s):  
Data Envelopment Analysis ◽  
Open Source ◽  
Optimization Method ◽  
Future Research ◽  
Data Envelopment ◽  
Software Projects ◽  
Data Set ◽  
Output Factors ◽  
Future Research Directions ◽  
Using Data

In this chapter, we propose for the first time a method to compare the efficiency of free and open source projects, based on the data envelopment analysis (DEA) methodology. DEA offers several advantages in this context, as it is a non-parametric optimization method without any need for the user to define any relations between different factors or a production function, can account for economies or diseconomies of scale, and is able to deal with multi-input, multi-output systems in which the factors have different scales. Using a data set of 43 large F/OS projects retrieved from SourceForge.net, we demonstrate the application of DEA, and show that DEA indeed is usable for comparing the efficiency of projects. We will also show additional analyses based on the results, exploring whether the inequality in work distribution within the projects, the licensing scheme or the intended audience have an effect on their efficiency. As this is a first attempt at using this method for F/OS projects, several future research directions are possible. These include additional work on determining input and output factors, comparisons within application areas, and comparison to commercial or mixed-mode development projects.

Study on distributed privacy preserving data mining

2014 ◽  
Vol 11 (2) ◽  
pp. 163-170
Author(s):  
Binli Wang ◽  
Yanguang Shen
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Rapid Development ◽  
Privacy Preserving ◽  
Future Research ◽  
Distributed Data ◽  
Distributed Environment ◽  
Privacy Preserving Data Mining ◽  
Advantages And Disadvantages ◽  
Future Research Directions

Recently, with the rapid development of network, communications and computer technology, privacy preserving data mining (PPDM) has become an increasingly important research in the field of data mining. In distributed environment, how to protect data privacy while doing data mining jobs from a large number of distributed data is more far-researching. This paper describes current research of PPDM at home and abroad. Then it puts emphasis on classifying the typical uses and algorithms of PPDM in distributed environment, and summarizing their advantages and disadvantages. Furthermore, it points out the future research directions in the field.

Measuring the Efficiency of Free and Open Source Software Projects Using Data Envelopment Analysis

2009 ◽  
pp. 2963-2977
Author(s):  
Stefan Koch
Keyword(s):  
Data Envelopment Analysis ◽  
Open Source ◽  
Optimization Method ◽  
Future Research ◽  
Data Envelopment ◽  
Software Projects ◽  
Data Set ◽  
Output Factors ◽  
Future Research Directions ◽  
Using Data

In this chapter, we propose for the first time a method to compare the efficiency of free and open source projects, based on the data envelopment analysis (DEA) methodology. DEA offers several advantages in this context, as it is a non-parametric optimization method without any need for the user to define any relations between different factors or a production function, can account for economies or diseconomies of scale, and is able to deal with multi-input, multi-output systems in which the factors have different scales. Using a data set of 43 large F/OS projects retrieved from SourceForge.net, we demonstrate the application of DEA, and show that DEA indeed is usable for comparing the efficiency of projects. We will also show additional analyses based on the results, exploring whether the inequality in work distribution within the projects, the licensing schem,e or the intended audience have an effect on their efficiency. As this is a first attempt at using this method for F/OS projects, several future research directions are possible. These include additional work on determining input and output factors, comparisons within application areas, and comparison to commercial or mixed-mode development projects.

Measuring Customer Satisfaction and Hotel Efficiency Analysis: An Approach Based on Data Envelopment Analysis

2020 ◽  
pp. 193896552094491
Author(s):  
Changhee Kim ◽  
Kyunghwa Chung
Keyword(s):  
Data Envelopment Analysis ◽  
Customer Satisfaction ◽  
Efficiency Score ◽  
Service Process ◽  
Future Research ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Analysis Model ◽  
Network Dea ◽  
Overall Efficiency

We propose a network DEA (Data Envelopment Analysis) model that consists of internal and external service processes and employs customer satisfaction as an intermediate factor. Using the proposed model, we calculate four efficiency scores: service productivity score drawn from internal service process, service efficiency score drawn from external service process, overall efficiency score drawn from both internal and external service processes, and management efficiency score calculated without the intermediate output. By analyzing the four efficiency scores, we find that overall efficiency score is well suited to represent a hotel’s comprehensive productivity. Our results support the validity of a network DEA model which includes customer satisfaction for analyzing hotel efficiency. Despite its important role that plays in hotel efficiency, customer satisfaction has been barely considered in the previous hotel efficiency studies. By analyzing hotel efficiency including customer satisfaction, this study sheds new light on the hotel efficiency research area and provides a valuable basis for future research.

Economies of scope in two-stage production systems: A data envelopment analysis approach

2018 ◽  
Vol 52 (2) ◽  
pp. 335-349 ◽  
Author(s):  
Leila Zeinalzadeh Ahranjani ◽  
Reza Kazemi Matin ◽  
Reza Farzipoor Saen
Keyword(s):  
Data Envelopment Analysis ◽  
Production Systems ◽  
Efficiency Score ◽  
Joint Production ◽  
Economies Of Scope ◽  
Data Envelopment ◽  
Two Stage ◽  
Network Dea ◽  
Second Stage ◽  
Dea Models

Traditional data envelopment analysis (DEA) models consider a production system as a black-box without taking into consideration its internal linked activities. In recent years, a number of DEA studies have been presented to estimate efficiency score of two-stage network production systems in which all outputs of the first stage (intermediate products) are used as inputs of the second stage to produce final outputs. This paper aims to develop a two-stage network DEA model to study economic notion of economies of scope (ES) between two products. It intends to determine profitability of joint production of two products by one firm. Numerical illustrations are presented to show applicability of proposed methods.

scholarly journals Data Envelopment Analysis Models in Non-Homogeneous Environment

2019 ◽  
Vol 67 (6) ◽  
pp. 1535-1540
Author(s):  
Josef Jablonský
Keyword(s):  
Data Envelopment Analysis ◽  
Parametric Method ◽  
Future Research ◽  
Data Envelopment ◽  
Weighted Sum ◽  
Homogeneity Assumption ◽  
Starting Point ◽  
Dea Models ◽  
And Performance ◽  
Homogeneous Environment

Data envelopment analysis (DEA) is a non-parametric method that is widely used for relative efficiency and performance evaluation of the set of decision-making units (DMUs). It is based on maximization of a weighted sum of outputs produced by the unit under evaluation divided by the weighted sum of inputs of the same unit, and the assumption that this ratio for all other units has to be lower or equal to 1. An important assumption for applications of DEA models is the homogeneity of the units. Unfortunately, the homogeneity assumption is not fulfilled in many real applications. The paper deals with the analysis of efficiency using DEA models in the non-homogeneous environment. One of the problems lies in non-homogeneous outputs. In this case, the units under evaluation spend the same inputs but produce completely or at least partly different set of outputs. The paper formulates several models how to deal with this problem and compares the results on a numerical example. Other main sources of non-homogeneity are discussed as an excellent possible starting point for future research.

A Review of Drug Side Effect Identification Methods

2020 ◽  
Vol 26 (26) ◽  
pp. 3096-3104 ◽  
Author(s):  
Shuai Deng ◽  
Yige Sun ◽  
Tianyi Zhao ◽  
Yang Hu ◽  
Tianyi Zang
Keyword(s):  
Side Effects ◽  
Future Research ◽  
Drug Side Effects ◽  
Important Indicator ◽  
Advantages And Disadvantages ◽  
Key Points ◽  
Network Methods ◽  
Future Research Directions ◽  
The One ◽  
Approved Drugs

Drug side effects have become an important indicator for evaluating the safety of drugs. There are two main factors in the frequent occurrence of drug safety problems; on the one hand, the clinical understanding of drug side effects is insufficient, leading to frequent adverse drug reactions, while on the other hand, due to the long-term period and complexity of clinical trials, side effects of approved drugs on the market cannot be reported in a timely manner. Therefore, many researchers have focused on developing methods to identify drug side effects. In this review, we summarize the methods of identifying drug side effects and common databases in this field. We classified methods of identifying side effects into four categories: biological experimental, machine learning, text mining and network methods. We point out the key points of each kind of method. In addition, we also explain the advantages and disadvantages of each method. Finally, we propose future research directions.

Measuring Performance of a Three-Stage Network Structure Using Data Envelopment Analysis and Nash Bargaining Game: A Supply Chain Application

2018 ◽  
Vol 17 (05) ◽  
pp. 1429-1467 ◽  
Author(s):  
Mohammad Amirkhan ◽  
Hosein Didehkhani ◽  
Kaveh Khalili-Damghani ◽  
Ashkan Hafezalkotob
Keyword(s):  
Supply Chain ◽  
Data Envelopment Analysis ◽  
Nash Bargaining ◽  
Data Envelopment ◽  
Second Stage ◽  
Proposed Model ◽  
Nash Bargaining Game ◽  
Dea Models ◽  
Overall Efficiency

The issue of efficiency analysis of network and multi-stage systems, as one of the most interesting fields in data envelopment analysis (DEA), has attracted much attention in recent years. A pure serial three-stage (PSTS) process is a specific kind of network in which all the outputs of the first stage are used as the only inputs in the second stage and in addition, all the outputs of the second stage are applied as the only inputs in the third stage. In this paper, a new three-stage DEA model is developed using the concept of three-player Nash bargaining game for PSTS processes. In this model, all of the stages cooperate together to improve the overall efficiency of main decision-making unit (DMU). In contrast to the centralized DEA models, the proposed model of this study provides a unique and fair decomposition of the overall efficiency among all three stages and eliminates probable confusion of centralized models for decomposing the overall efficiency score. Some theoretical aspects of proposed model, including convexity and compactness of feasible region, are discussed. Since the proposed bargaining model is a nonlinear mathematical programming, a heuristic linearization approach is also provided. A numerical example and a real-life case study in supply chain are provided to check the efficacy and applicability of the proposed model. The results of proposed model on both numerical example and real case study are compared with those of existing centralized DEA models in the literature. The comparison reveals the efficacy and suitability of proposed model while the pitfalls of centralized DEA model are also resolved. A comprehensive sensitivity analysis is also conducted on the breakdown point associated with each stage.

Rationality, accounting and benchmarking water businesses

2018 ◽  
Vol 31 (3) ◽  
pp. 290-315 ◽  
Author(s):  
Nicholas Pawsey ◽  
Jayanath Ananda ◽  
Zahirul Hoque
Keyword(s):  
Economic Efficiency ◽  
Fair Value ◽  
Water Utilities ◽  
Future Research ◽  
Data Envelopment ◽  
Water Utility ◽  
Capital Input ◽  
Content Type ◽  
Dea Models ◽  
Future Work

Purpose The purpose of this paper is to explore the sensitivity of economic efficiency rankings of water businesses to the choice of alternative physical and accounting capital input measures. Design/methodology/approach Data envelopment analysis (DEA) was used to compute efficiency rankings for government-owned water businesses from the state of Victoria, Australia, over the period 2005/2006 through 2012/2013. Differences between DEA models when capital inputs were measured using either: statutory accounting values (historic cost and fair value), physical measures, or regulatory accounting values, were scrutinised. Findings Depending on the choice of capital input, significant variation in efficiency scores and the ranking of the top (worst) performing firms was observed. Research limitations/implications Future research may explore the generalisability of findings to a wider sample of water utilities globally. Future work can also consider the most reliable treatment of capital inputs in efficiency analysis. Practical implications Regulators should be cautious when using economic efficiency data in benchmarking exercises. A consistent approach to account for the capital stock is needed in the determination of price caps and designing incentives for poor performers. Originality/value DEA has been widely used to explore the role of ownership structure, firm size and regulation on water utility efficiency. This is the first study of its kind to explore the sensitivity of DEA to alternative physical and accounting capital input measures. This research also improves the conventional performance measurement in water utilities by using a bootstrap procedure to address the deterministic nature of the DEA approach.

scholarly journals A Range Adjusted Measure of Super-Efficiency in Integer-Valued Data Envelopment Analysis with Undesirable Outputs

2021 ◽  
Vol 9 (4) ◽  
pp. 378-398
Author(s):  
Chunhua Chen ◽  
Haohua Liu ◽  
Lijun Tang ◽  
Jianwei Ren
Keyword(s):  
Data Envelopment Analysis ◽  
Transportation Systems ◽  
Decision Makers ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Super Efficiency ◽  
Dea Model ◽  
Dea Models ◽  
Analysis Models ◽  
Transport Accidents

Abstract DEA (data envelopment analysis) models can be divided into two groups: Radial DEA and non-radial DEA, and the latter has higher discriminatory power than the former. The range adjusted measure (RAM) is an effective and widely used non-radial DEA approach. However, to the best of our knowledge, there is no literature on the integer-valued super-efficiency RAM-DEA model, especially when undesirable outputs are included. We first propose an integer-valued RAM-DEA model with undesirable outputs and then extend this model to an integer-valued super-efficiency RAM-DEA model with undesirable outputs. Compared with other DEA models, the two novel models have many advantages: 1) They are non-oriented and non-radial DEA models, which enable decision makers to simultaneously and non-proportionally improve inputs and outputs; 2) They can handle integer-valued variables and undesirable outputs, so the results obtained are more reliable; 3) The results can be easily obtained as it is based on linear programming; 4) The integer-valued super-efficiency RAM-DEA model with undesirable outputs can be used to accurately rank efficient DMUs. The proposed models are applied to evaluate the efficiency of China’s regional transportation systems (RTSs) considering the number of transport accidents (an undesirable output). The results help decision makers improve the performance of inefficient RTSs and analyze the strengths of efficient RTSs.

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