A semi-oriented radial measure for measuring the efficiency of decision making units with negative data, using DEA

Data Envelopment Analysis (DEA) is a nonparametric method for measuring the efficiency of a set of decision making units such as firms or public sector agencies, first introduced into the operational research and management science literature by Charnes, Cooper, and Rhodes (CCR) [Charnes, A., Cooper, W.W., Rhodes, E., 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2, 429–444]. The original DEA models were applicable only to technologies characterized by positive inputs/outputs. In subsequent literature there have been various approaches to enable DEA to deal with negative data. In this paper, we propose a semi-oriented radial measure, which permits the presence of variables which can take both negative and positive values. The model is applied to data on a notional effluent processing system to compare the results with those yielded by two alternative methods for dealing with negative data in DEA: The modified slacks-based model suggested by Sharp et al. [Sharp, J.A., Liu, W.B., Meng, W., 2006. A modified slacks-based measure model for data envelopment analysis with ‘natural’ negative outputs and inputs. Journal of Operational Research Society 57 (11) 1–6] and the range directional model developed by Portela et al. [Portela, M.C.A.S., Thanassoulis, E., Simpson, G., 2004. A directional distance approach to deal with negative data in DEA: An application to bank branches. Journal of Operational Research Society 55 (10) 1111–1121]. A further example explores the advantages of using the new model.

Ranking efficient decision-making units in data envelopment analysis using fuzzy concept

This paper introduces a new mathematical method for improving the discrimination power of data envelopment analysis and to completely rank the efficient decision-making units (DMUs). Fuzzy concept is utilised. For this purpose, first all DMUs are evaluated with the CCR model. Thereafter, the resulted weights for each output are considered as fuzzy sets and are then converted to fuzzy numbers. The introduced model is a multi-objective linear model, endpoints of which are the highest and lowest of the weighted values. An added advantage of the model is its ability to handle the infeasibility situation sometimes faced by previously introduced models.

Advances in data envelopment analysis

Since its introduction in 1978, data envelopment analysis (DEA) has become one of the preeminent nonparametric methods for measuring efficiency and productivity of decision making units (DMUs). Charnes et al. (1978) provided the original DEA constant returns to scale (CRS) model, later extended to variable returns to scale (VRS) by Banker et al. (1984). These ‘standard’ models are known by the acronyms CCR and BCC, respectively, and are now employed routinely in areas that range from assessment of public sectors, such as hospitals and health care systems, schools, and universities, to private sectors, such as banks and financial institutions (Emrouznejad et al. 2008; Emrouznejad and De Witte 2010). The main objective of this volume is to publish original studies that are beyond the two standard CCR and BCC models with both theoretical and practical applications using advanced models in DEA.

A bi-objective weighted model for improving the discrimination power in MCDEA

Lack of discrimination power and poor weight dispersion remain major issues in Data Envelopment Analysis (DEA). Since the initial multiple criteria DEA (MCDEA) model developed in the late 1990s, only goal programming approaches; that is, the GPDEA-CCR and GPDEA-BCC were introduced for solving the said problems in a multi-objective framework. We found GPDEA models to be invalid and demonstrate that our proposed bi-objective multiple criteria DEA (BiO-MCDEA) outperforms the GPDEA models in the aspects of discrimination power and weight dispersion, as well as requiring less computational codes. An application of energy dependency among 25 European Union member countries is further used to describe the efficacy of our approach. © 2013 Elsevier B.V. All rights reserved.

A fuzzy expected value approach under generalized data envelopment analysis

Fuzzy data envelopment analysis (DEA) models emerge as another class of DEA models to account for imprecise inputs and outputs for decision making units (DMUs). Although several approaches for solving fuzzy DEA models have been developed, there are some drawbacks, ranging from the inability to provide satisfactory discrimination power to simplistic numerical examples that handles only triangular fuzzy numbers or symmetrical fuzzy numbers. To address these drawbacks, this paper proposes using the concept of expected value in generalized DEA (GDEA) model. This allows the unification of three models - fuzzy expected CCR, fuzzy expected BCC, and fuzzy expected FDH models - and the ability of these models to handle both symmetrical and asymmetrical fuzzy numbers. We also explored the role of fuzzy GDEA model as a ranking method and compared it to existing super-efficiency evaluation models. Our proposed model is always feasible, while infeasibility problems remain in certain cases under existing super-efficiency models. In order to illustrate the performance of the proposed method, it is first tested using two established numerical examples and compared with the results obtained from alternative methods. A third example on energy dependency among 23 European Union (EU) member countries is further used to validate and describe the efficacy of our approach under asymmetric fuzzy numbers.

Monocyte subpopulation counts and TNF alpha associations with clinical outcome post ST elevation myocardial infarction

Background Monocytes are implicated in the initiation and progression of the atherosclerotic plaque contributing to its instability and rupture. Although peripheral monocytosis has been related to poor clinical outcome post ST elevation myocardial infarction (STEMI), only scarce information is available of mechanisms of this association. Tumour necrosis factor alpha (TNFα) is a key cytokine in the acute phase inflammatory response, and it is predominantly produced by inflammatory macrophages. Little is known about TNFα association with circulating monocyte subpopulations post STEMI. Method A total of 142 STEMI patients (mean age 62±13 years; 72% male) treated with percutaneous revascularization were recruited with blood samples obtained within first 24 hours from the onset and on day 10-14. Peripheral blood monocyte subpopulations were enumerated and characterized using flow cytometry after staining for CD14, CD16 and CCR2 and were defined as: CD14++CD16-CCR2+ (Mon1), CD14++CD16+CCR+ (Mon2) and CD14+CD16++CCR2- (Mon3) cells. Plasma levels of TNFα were measured by enzyme-linked immunosorbent assay (ELISA, Peprotec system, UK). Major adverse cardiac events (MACE), defined as recurrent STEMI, new diagnosis of heart failure and death were recorded at follow up, mean of 164±134 days. Results TNFα levels were significantly higher 24 hours post STEMI, compared to day 14 (paired t-test, p <0.001) with day 1 levels weakly correlated with total monocyte count as well as Mon1 (Spearman’s correlation, r=0.19, p=0.02 and r=0.22, p=0.01, respectively). There was no correlation between TNFα and Mon2 or Mon3 subpopulations. TNFα levels were significantly higher in patients with a recorded MACE (n=28, Mann-Whitney test, p<0.001) (figure 1).⇓

Monocyte subset counts associations with left ventricular systolic function post ST elevation myocardial infarction

Background: Monocytes are implicated in the initiation and progression of theatherosclerotic plaque contributing to plaque instability and rupture. Little is knownof the role played by the 3 phenotypically and functionally different monocytesubpopulations in determining ventricular remodeling following ST elevation my-ocardial infarction (STEMI). Mon1 are "classical" inflammatory monocytes, whilstMon3 are considered reparative with fibroblast deposition ability. The function ofthe newly described Mon2 is yet to be elucidated. Method: STEMI patients (n=196, mean age 62±13 years; 72% male) treatedwith percutaneous revascularization were recruited within the first 24 hours. Pe-ripheral blood monocyte subpopulations were enumerated and characterizedusing flow cytometry after staining for CD14, CD16 and CCR2. Phenotypi-cally, monocyte subpopulations are defined as: CD14+CD16-CCR2+ (Mon1),CD14+CD16+CCR+ (Mon2) and CD14lowCD16+CCR2- (Mon3) cells. Transtho-racic 2D echocardiography was performed within 7 days and 6 months post infarctto assess ventricular volumes, mass, systolic, and diastolic functions. Results: Using linear regression analysis higher counts for Mon1, and lowercounts for Mon2 and Mon3 were significantly associated with the baseline leftventricular ejection fraction (LVEF) within seven days post infarction. At 6 monthspost STEMI lower counts of Mon2 remained positively associated with decreasedLVEF (p value= 0.002).Monocyte subsets correlation with LVEFMonocytes mean florescence Baseline left ventricular Left ventricular ejectionintensity (cells/μl) ejection fraction (%) fraction (%) at 6 months post infarctβ-value P-valueβ-value P-valueTotal Mon0.31 P<0.001 0.360.009Mon 10.019 0.020.070.62Mon 2−0.28 0.001 −0.420.002Mon 3−0.27 0.001 −0.180.21 Conclusion: Peripheral monocytes of all three subsets correlate with LVEF af-ter a myocardial infarction. High counts of the inflammatory Mon1 are associatedwith reduction in the baseline LVEF. Post remodelling, the convalescent EF wasindependently predicted by monocyte subpopulation 2. As lower counts depictednegative ventricular remodeling, this suggests a reparative role for the newly de-scribed Mon2, possibly via myofibroblast deposition and angiogenesis, in contrastto an anticipated inflammatory role.