An input relaxation model for evaluating congestion in fuzzy DEA

Kheirollahi, Hooshang; Hessari, Peyman; Charles, Vincent; Chawshini, Rasoul

doi:10.17535/crorr.2017.0025

Cited by 8 publications

(2 citation statements)

References 27 publications

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“…As we saw in Table 1, the units 2, 3,11,12,13,14,15,18,19,20,21,26 are weakly congested and the units 23 and 25 are stronly congested by TS method. On the other hand, in our proposed method for the second scenario, we obtain the input direction vector w o 0 and the output direction vector δ o 0 for all o ∈ {2, 3, 11, 12, 13, 14, 15, 18, 19, 20, 21, 26}, so, according to Theorem 3.3, the units 2, 3,11,12,13,14,15,18,19,20,21,26 are weakly congested by our proposed method. Also, we obtain the input direction vector w o > 0 and the output direction vector δ o > 0 for o ∈ {23, 25} , so, according to Theorem 3.4, DMU 23 and DMU 25 are strongly congested units by our proposed method.…”

Section: Numerical Examplesmentioning

confidence: 86%

A full investigation of the directional congestion in data envelopment analysis

2021

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One of interesting subjects in Data Envelopment Analysis (DEA) is estimation of congestion of Decision Making Units (DMUs). Congestion is evidenced when decreases (increases) in some inputs re- sult in increases (decreases) in some outputs without worsening (im- proving) any other input/output. Most of the existing methods for measuring the congestion of DMUs utilize the traditional de nition of congestion and assume that inputs and outputs change with the same proportion. Therefore, the important question that arises is whether congestion will occur or not if the decision maker (DM) increases or de- creases the inputs dis-proportionally. This means that, the traditional de nition of congestion in DEA may be unable to measure the con- gestion of units with multiple inputs and outputs. This paper focuses on the directional congestion and proposes methods for recognizing the directional congestion using DEA models. To do this, we consider two di erent scenarios: (i) just the input direction is available. (ii) none of the input and output directions are available. For each scenario, we propose a method consists in systems of inequalities or linear pro- gramming problems for estimation of the directional congestion. The validity of the proposed methods are demonstrated utilizing two nu- merical examples.

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Section: Numerical Examplesmentioning

confidence: 86%

A full investigation of the directional congestion in data envelopment analysis

2021

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“…It should be noted that UP approaches include stochastic optimization (SO), fuzzy optimization (FO), robust optimization (RO), bootstrap (BS), interval programming (IP), neutrosophic theory (NT), Z-number theory (ZT), and uncertainty theory (UT). [36] Charnes-Cooper-Rhodes ✓ Ebrahimnejad [37] Cost Efficiency ✓ Hatami-Marbini et al [38] Malmquist Productivity Index ✓ Khaki et al [39] Charnes-Cooper-Rhodes ✓ Costantino et al [40] Cross Efficiency ✓ De Nicola et al [41] Banker-Charnes-Cooper ✓ Khodaparasti and Maleki [42] Simultaneous Dynamic ✓ Kalantary and Azar [43] Charnes-Cooper-Rhodes ✓ Karadayi and Karsak [44] Charnes-Cooper-Rhodes ✓ Dotoli et al [45] Cross Efficiency ✓ Haji-Sami et al [46] Charnes-Cooper-Rhodes ✓ Kheirollahi et al [47] Congestion ✓ Mitropoulos et al [48] Charnes-Cooper-Rhodes ✓ Rabbani et al [49] Charnes-Cooper-Rhodes ✓ ✓ ✓ Shwartz et al [50] Banker-Charnes-Cooper ✓ Arya and Yadav [51] Slacks-Based Measure ✓ Karsak and Karadayi [52] Charnes-Cooper-Rhodes ✓ Kheirollahi et al [53] Congestion ✓ Otay et al [54] Charnes-Cooper-Rhodes ✓ Ahmadvand and Pishvaee [55] Common Set of Weights ✓ Peykani et al [56] Malmquist Productivity Index ✓ Wu and Wu [57] Charnes-Cooper-Rhodes ✓ Hatefi and Haeri [58] Charnes-Cooper-Rhodes ✓ Ji et al [59] Charnes-Cooper-Rhodes ✓ Peykani et al [60] Charnes-Cooper-Rhodes ✓ Arya and Yadav [61] Charnes-Cooper-Rhodes ✓ Ghafari Someh et al [62] Three-Stage Network ✓ Yang et al [63] Charnes-Cooper-Rhodes ✓ Abdelfattah [64] Charnes-Cooper-Rhodes ✓ Gómez-Gallego et al [65] Banker-Charnes-Cooper ✓ Izadikhah et al [66] Charnes-Cooper-Rhodes ✓ ✓ Jahani and Kordrostami [67] Slacks-Based Measure ✓ Tavana et al [68] Charnes-Coo...…”

Section: Literature Reviewmentioning

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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A new method of determining decision-making unit congestion under inter-temporal dependence

et al. 2022

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Congestion is an important concept in data envelopment analysis (DEA). It occurs when the decrease of at least one decision making unit (DMU) input results in the increase of at least one output ineffective on other inputs or outputs. Until present, various methods have been discussed in terms of congestion determination in static DEA although no study has still been performed on congestion calculation in dynamic DEA under inter-temporal dependence. In this paper, congested input levels in multi-period production systems under inter-temporal dependence and further, the inter-temporal dependence of production periods due to the stock capital are studied. The advantage of dynamic DEA congestion method specification lies in the prevention of inappropriate resources allocation during evaluation periods. The validity of the proposed method has further been demonstrated via empirical example for better observation.

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An input relaxation model for evaluating congestion in fuzzy DEA

Cited by 8 publications

References 27 publications

A full investigation of the directional congestion in data envelopment analysis

A full investigation of the directional congestion in data envelopment analysis

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

A new method of determining decision-making unit congestion under inter-temporal dependence

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