Abstract
Several network-data envelopment analysis (DEA) performance assessment models have been proposed in the literature;
however, the conflicts between stages and insufficient number of decision-making units (DMUs) challenge the researchers.
In this paper, a novel game-DEA model is proposed for efficiency assessment of network structure DMUs. We propose a
two-stage modeling, where in the first stage network is divided into several sub-networks; we at the same time categorize
input variables to measure efficiency of sub-networks within each input category. In the second stage, we calculate
efficiency of the network by aggregating efficiency scores of sub-networks within each category. In this way, the issue of
insufficient number of DMUs when there are many input/output variables can be handled as well. One of the main
contributions of this paper is assuming each category and stage as a player in Nash bargaining game. Using the concept
borrowed from Nash bargaining game model, the proposed game-DEA model tries to maximize distances of efficiency
scores of each player form their corresponding breakdown points. The usefulness of the model is presented using a real
case study to measure the efficiency of bank branches.
however, the conflicts between stages and insufficient number of decision-making units (DMUs) challenge the researchers.
In this paper, a novel game-DEA model is proposed for efficiency assessment of network structure DMUs. We propose a
two-stage modeling, where in the first stage network is divided into several sub-networks; we at the same time categorize
input variables to measure efficiency of sub-networks within each input category. In the second stage, we calculate
efficiency of the network by aggregating efficiency scores of sub-networks within each category. In this way, the issue of
insufficient number of DMUs when there are many input/output variables can be handled as well. One of the main
contributions of this paper is assuming each category and stage as a player in Nash bargaining game. Using the concept
borrowed from Nash bargaining game model, the proposed game-DEA model tries to maximize distances of efficiency
scores of each player form their corresponding breakdown points. The usefulness of the model is presented using a real
case study to measure the efficiency of bank branches.
| Original language | English |
|---|---|
| Pages (from-to) | 6429–6447 |
| Number of pages | 19 |
| Journal | Neural Computing and Applications |
| Volume | 31 |
| Early online date | 7 Apr 2018 |
| DOIs | |
| Publication status | Published - 7 Oct 2019 |
Bibliographical note
© The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Keywords
- Data envelopment analysis
- Network DEA
- Game DEA
- Bargaining game
- Performance assessment
- Additional inputs
- Banking