A robust DEA model under discrete scenarios for assessing bank branches

Omrani, Hashem; Shamsi, Meisam; Emrouznejad, Ali; Теплова, Тамара

doi:10.1016/j.eswa.2023.119694

Cited by 21 publications

(9 citation statements)

References 60 publications

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“…Future studies could address this limitation by incorporating a more comprehensive approach that considers the broader contextual factors influencing hospital performance within uncertain environments. Also, to further advance research in this field, alternative and widely used uncertain programming techniques, like uncertainty set-based robust optimization [ 49 , 98 , 99 , 100 , 101 ], scenario-based robust optimization [ 102 , 103 , 104 , 105 , 106 ], robust possibilistic programming [ 107 , 108 , 109 , 110 , 111 ], and fuzzy chance-constrained programming [ 112 , 113 , 114 , 115 , 116 ], can be incorporated to effectively address the challenges posed by data uncertainty.…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Performance Evaluation of Hospitals under Data Uncertainty: An Uncertain Common-Weights Data Envelopment Analysis

Peykani,

Pishvaee

2024

Healthcare

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In the context of healthcare systems, the performance evaluation of hospitals plays a crucial role in assessing the quality of healthcare systems and facilitating informed decision-making processes. However, the presence of data uncertainty poses significant challenges to accurate performance measurement. This paper presents a novel uncertain common-weights data envelopment analysis (UCWDEA) approach for evaluating the performance of hospitals under uncertain environments. The proposed UCWDEA approach addresses the limitations of traditional data envelopment analysis (DEA) models by incorporating the uncertainty theory (UT) to model the inherent uncertainty in input and output data. Also, by utilizing a common set of weights (CSW) technique, the UCWDEA method provides a more robust and reliable assessment of hospital performance. The main advantages of the proposed UCWDEA approach can be succinctly summarized as follows. Firstly, it allows for the comparison of all hospitals on a consistent basis to calculate a realistic efficiency score, rather than an overly optimistic efficiency score. Secondly, the uncertain common-weights DEA approach exhibits linearity, enhancing its applicability. Thirdly, it possesses the capability to extend its utility under various other prevalent uncertainty distributions. Moreover, it enhances the discriminatory power of results, facilitates the ranking of hospitals in the presence of data uncertainty, and aids in identifying the sensitivity and stability levels of hospitals towards data uncertainty. Notably, in order to showcase the pragmatic application and efficacy of the uncertain common-weights DEA model, a genuine dataset has been utilized to evaluate the efficiency of 20 public hospitals in Tehran, all of which are affiliated with the Iran University of Medical Sciences. The results of the experiment demonstrate the efficacy of the UCWDEA approach in assessing and ranking hospitals amidst uncertain conditions. In summary, the research outcomes can offer policymakers valuable insights regarding hospital performance amidst data uncertainty. Additionally, it can provide practical recommendations on optimizing resource allocation, benchmarking performance, and formulating effective policies to augment the overall efficiency and effectiveness of healthcare services.

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Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Performance Evaluation of Hospitals under Data Uncertainty: An Uncertain Common-Weights Data Envelopment Analysis

Peykani,

Pishvaee

2024

Healthcare

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“…In [17] a medical facility location problem with multiple patient flows between demand points and medical facilities is solved using a multiobjective tabu search algorithm. In [18] authors extend the classic data envelopment analyses by weighting all inputs and outputs and solving one linear programming model; and using a robust approach to handle the uncertainty in data with data of discrete scenarios coupled with a fuzzy programming method.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Location Problem for Bank Branches

Montero,

Nicolis,

Reid

et al. 2023

IEEE Access

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Although current technologies allow simple online banking transactions, it is vital to guarantee access to bank branches both for customers and businesses. Banking branch interactions improve trust in bank institutions in everyday situations and emergencies. This research addresses the problem of the location of banking facilities. A set of objective functions related to socio-economic variables, geographical coverage and bankers preferences are considered. A multi-objective linear programming model is proposed to solve different versions of the problem considering a maximum radial distance coverage. The (mono/bi/multi) objective versions of the problem are analyzed by varying the values of the weights of objective functions in the proposed model that is then solved using AMPL Gurobi solver. The case study is carried out in Santiago de Chile with data from a local banking institution. In the results, the bank's initial situation is analyzed to contrast the solutions found later. This analysis is performed considering the mono-objective, bi-objective, and multi-objective nature of the studied problem. In all these cases, we analyzed changes in the structure of solutions as the coverage of the branches increases. The proposed model can quickly obtain feasible solutions according to the needs and available resources, allowing to replicate the analysis in different institutions under similar conditions considering strategically established priorities.

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“…The number of studies that deal with stochasticity in the DEA framework is continuously growing. Omrani et al [17,18] developed a multi-objective DEA model to determine three types of efficiency, i.e., profitability, operational and transactional for bank branches with uncertain data. The uncertainty in data is treated using discrete scenarios.…”

Section: A Stochastic Bilevel Dea Model For Centralized Resource Allo...mentioning

confidence: 99%