Network DEA II

2014 ◽  
pp. 307-327 ◽  
Author(s):  
Rolf Färe ◽  
Shawna Grosskopf ◽  
Gerald Whittaker
Keyword(s):  
Network Dea

scholarly journals A Fuzzy Network DEA Approach to the Selection of Advanced Manufacturing Technology

2021 ◽  
Vol 13 (8) ◽  
pp. 4236
Author(s):  
Tim Lu
Keyword(s):  
Fuzzy Numbers ◽  
Weighted Average ◽  
Careful Consideration ◽  
Performance Criteria ◽  
Advanced Manufacturing ◽  
Weight Restrictions ◽  
Network Dea ◽  
Engineering Sciences ◽  
Fuzzy Network ◽  
Selection Of

The selection of advanced manufacturing technologies (AMTs) is an essential yet complex decision that requires careful consideration of various performance criteria. In real-world applications, there are cases that observations are difficult to measure precisely, observations are represented as linguistic terms, or the data need to be estimated. Since the growth of engineering sciences has been the key reason for the increased utilization of AMTs, this paper develops a fuzzy network data envelopment analysis (DEA) to the selection of AMT alternatives considering multiple decision-makers (DMs) and weight restrictions when the input and output data are represented as fuzzy numbers. By viewing the multiple DMs as a network one, the data provided by each DM can then be taken into account in evaluating the overall performances of AMT alternatives. In the solution process, we obtain the overall and DMs efficiency scores of each AMT alternative at the same time, and a relationship in which the former is a weighted average of the latter is also derived. Since the final evaluation results of AMTs are fuzzy numbers, a ranking procedure is employed to determine the most preferred one. An example is used to illustrate the applicability of the proposed methodology.

Proportional trade-offs in network DEA

2021 ◽  
pp. 107285
Author(s):  
Fatemeh Boolori ◽  
Rashed Khanjani Shiraz ◽  
Hirofumi Fukuyama
Keyword(s):  
Network Dea ◽  
Trade Offs

scholarly journals How to evaluate sustainability of supply chains? A dynamic network DEA approach

2017 ◽  
Vol 117 (9) ◽  
pp. 1866-1889 ◽  
Author(s):  
Vahid Shokri Kahi ◽  
Saeed Yousefi ◽  
Hadi Shabanpour ◽  
Reza Farzipoor Saen
Keyword(s):  
Big Data ◽  
Supply Chains ◽  
Real World ◽  
Performance Optimization ◽  
Network Dynamic ◽  
Network Dea ◽  
Content Type ◽  
Dea Model ◽  
Proposed Model ◽  
First Time

Purpose The purpose of this paper is to develop a novel network and dynamic data envelopment analysis (DEA) model for evaluating sustainability of supply chains. In the proposed model, all links can be considered in calculation of efficiency score. Design/methodology/approach A dynamic DEA model to evaluate sustainable supply chains in which networks have series structure is proposed. Nature of free links is defined and subsequently applied in calculating relative efficiency of supply chains. An additive network DEA model is developed to evaluate sustainability of supply chains in several periods. A case study demonstrates applicability of proposed approach. Findings This paper assists managers to identify inefficient supply chains and take proper remedial actions for performance optimization. Besides, overall efficiency scores of supply chains have less fluctuation. By utilizing the proposed model and determining dual-role factors, managers can plan their supply chains properly and more accurately. Research limitations/implications In real world, managers face with big data. Therefore, we need to develop an approach to deal with big data. Practical implications The proposed model offers useful managerial implications along with means for managers to monitor and measure efficiency of their production processes. The proposed model can be applied in real world problems in which decision makers are faced with multi-stage processes such as supply chains, production systems, etc. Originality/value For the first time, the authors present additive model of network-dynamic DEA. For the first time, the authors outline the links in a way that carry-overs of networks are connected in different periods and not in different stages.

Composition versus decomposition in two-stage network DEA: a reverse approach

2014 ◽  
Vol 45 (1) ◽  
pp. 71-87 ◽  
Author(s):  
Dimitris K. Despotis ◽  
Gregory Koronakos ◽  
Dimitris Sotiros
Keyword(s):  
Two Stage ◽  
Network Dea

A Novel Enhanced Russell Graph Efficiency Measure in Network DEA

2021 ◽  
pp. 1-31
Author(s):  
Robabeh Eslami ◽  
Mohammad Khoveyni
Keyword(s):  
Efficiency Score ◽  
Network System ◽  
Network Structures ◽  
Efficiency Measure ◽  
Network Dea ◽  
Economic Implications ◽  
Multi Stage ◽  
Proposed Model ◽  
Decision Making Units ◽  
High Computational Complexity

Hitherto, the presented models for measuring the efficiency score of multi-stage decision-making units (DMUs) either are nonlinear or require to specify the weights for combining their divisional efficiencies. The nonlinearity leads to high computational complexity for these models, especially when used for problems with enormous dimensions, and also assigning various weights to the divisional efficiencies causes to obtain different efficiency scores for the multi-stage network system. To tackle these problems, this study contributes to network DEA by introducing a novel enhanced Russell graph (ERG) efficiency measure for evaluating the general two-stage series network structures. Then, the proposed model is extended into the general multi-stage series network structures. This study also describes the managerial and economic implications of measuring the efficiency score of the multi-stage DMUs and provides two numerical and empirical examples for illustrating the use of our proposed model.

Research on Innovation Efficiency and Technology Gap in China Economic Development

2017 ◽  
Vol 34 (02) ◽  
pp. 1750005 ◽  
Author(s):  
Jian-Wen Fang ◽  
Yung-ho Chiu
Keyword(s):  
Economic Development ◽  
Fixed Effect Model ◽  
University Research ◽  
Quadrant Analysis ◽  
Eastern Region ◽  
Network Dea ◽  
Research Cooperation ◽  
Effect Model ◽  
Technology Gaps ◽  
Technology Level

In this paper, we use the meta-frontier network DEA approach to evaluate the innovation efficiency of 30 provinces in China from 2009 to 2011. These provinces have been classified into two groups based on their levels of economic development. The first group comprises provinces in the Eastern region, while the second group comprises provinces in the Central and Western regions. First, we use the meta-frontier network DEA method to estimate the technology gaps of innovation efficiency between different operating types. Second, the quadrant analysis method explores the reasons for efficiency losses. Finally, we take the fixed effect model to examine whether industry–university–research cooperation influences technology efficiency. The empirical results indicate (i) the Eastern region has significantly higher innovation efficiency than the Central and Western regions. (ii) Some Eastern provinces have a high technology level, yet their resource allocation capabilities still need to be improved. (iii) Industry–university–research cooperation is an effective way to improve innovation performance.

Sign in / Sign up

Export Citation Format

Share Document