Measuring the efficiency of two stage network processes: a satisficing DEA approach
Publication date
2021Keyword
Stochastic DEASatisficing DEA
Chance constrained model
Two stage system
Performance evaluation
Efficiency
Peer-Reviewed
YesOpen Access status
closedAccessAccepted for publication
29/08/2019
Metadata
Show full item recordAbstract
Regular Network Data Envelopment Analysis (NDEA) models deal with evaluating the performance of a set of decision-making units (DMUs) with a two-stage construction in the context of a deterministic data set. In the real world, however, observations may display a stochastic behavior. To the best of our knowledge, despite the existing research done with different data types, studies on two-stage processes with stochastic data are still very limited. This paper proposes a two-stage network DEA model with stochastic data. The stochastic two-stage network DEA model is formulated based on the satisficing DEA models of chance-constrained programming and the leader-follower concepts. According to the probability distribution properties and under the assumption of the single random factor of the data, the probabilistic form of the model is transformed into its equivalent deterministic linear programming model. In addition, the relationship between the two stages as the leader and the follower, respectively, at different confidence levels and under different aspiration levels, is discussed. The proposed model is further applied to a real case concerning 16 commercial banks in China in order to confirm the applicability of the proposed approach at different confidence levels and under different aspiration levels.Version
No full-text in the repositoryCitation
Mehdizadeh S, Amirteimoori A, Vincent C et al (2021) Measuring the efficiency of two stage network processes: a satisficing DEA approach. Journal of the Operational Research Society. 72(2): 354-366.Link to Version of Record
https://doi.org/10.1080/01605682.2019.1671151Type
Articleae974a485f413a2113503eed53cd6c53
https://doi.org/10.1080/01605682.2019.1671151