Robust optimization in data envelopment analysis: extended theory and applications.

Performance evaluation of decision-making units (DMUs) via the data envelopment analysis (DEA) is confronted with multi-conflicting objectives, complex alternatives and significant uncertainties. Visualizing the risk of uncertainties in the data used in the evaluation process is crucial to understanding the need for cutting edge solution techniques to organizational decisions. A greater management concern is to have techniques and practical models that can evaluate their operations and make decisions that are not only optimal but also consistent with the changing environment. Motivated by the myriad need to mitigate the risk of uncertainties in performance evaluations, this thesis focuses on finding robust and flexible evaluation strategies to the ranking and classification of DMUs. It studies performance measurement with the DEA tool and addresses the uncertainties in data via the robust optimization technique. The thesis develops new models in robust data envelopment analysis with applications to management science, which are pursued in four research thrust. In the first thrust, a robust counterpart optimization with nonnegative decision variables is proposed which is then used to formulate new budget of uncertainty-based robust DEA models. The proposed model is shown to save the computational cost for robust optimization solutions to operations research problems involving only positive decision variables. The second research thrust studies the duality relations of models within the worst-case and best-case approach in the input \u2013 output orientation framework. A key contribution is the design of a classification scheme that utilizes the conservativeness and the risk preference of the decision maker. In the third thrust, a new robust DEA model based on ellipsoidal uncertainty sets is proposed which is further extended to the additive model and compared with imprecise additive models. The final thrust study the modelling techniques including goal programming, robust optimization and data envelopment to a transportation problem where the concern is on the efficiency of the transport network, uncertainties in the demand and supply of goods and a compromising solution to multiple conflicting objectives of the decision maker. Several numerical examples and real-world applications are made to explore and demonstrate the applicability of the developed models and their essence to management decisions. Applications such as the robust evaluation of banking efficiency in Europe and in particular Germany and Italy are made. Considering the proposed models and their applications, efficiency analysis explored in this research will correspond to the practical framework of industrial and organizational decision making and will further advance the course of robust management decisions

Robust data envelopment analysis via ellipsoidal uncertainty sets with application to the Italian banking industry

AbstractThis paper extends the conventional DEA models to a robust DEA (RDEA) framework by proposing new models for evaluating the efficiency of a set of homogeneous decision-making units (DMUs) under ellipsoidal uncertainty sets. Four main contributions are made: (1) we propose new RDEA models based on two uncertainty sets: an ellipsoidal set that models unbounded and correlated uncertainties and an interval-based ellipsoidal uncertainty set that models bounded and correlated uncertainties, and study the relationship between the RDEA models of these two sets, (2) we provide a robust classification scheme where DMUs can be classified into fully robust efficient, partially robust efficient and robust inefficient, (3) the proposed models are extended to the additive DEA model and its efficacy is analyzed with two imprecise additive DEA models in the literature, and finally, (4) we apply the proposed models to study the performance of banks in the Italian banking industry. We show that few banks which were resilient in their performance can be robustly classified as partially efficient or fully efficient in an uncertain environment

The impact of inward FDI on output growth volatility: A country-sector analysis

Abstract While existing literature points to a positive impact of FDI on host countries' growth, little is known about how inward FDI contributes to economic volatility in the host country. In this paper, we investigate the FDI-output growth volatility nexus focusing on manufacturing sectors of OECD countries over the period 1990 to 2015. We document a positive and statistically significant relationship between inward FDI stock and sectoral output volatility. We also show that the impact of inward FDI stock in downstream activities on volatility is larger compared to that of inward FDI stock in upstream activities which is not significant. Additionally, we find that the positive relationship between FDI and volatility is stronger in high capital-intensive industries. These results are robust to the use of a measure of FDI targeting practices

Robust optimization in data envelopment analysis: extended theory and applications.

Performance evaluation of decision-making units (DMUs) via the data envelopment analysis (DEA) is confronted with multi-conflicting objectives, complex alternatives and significant uncertainties. Visualizing the risk of uncertainties in the data used in the evaluation process is crucial to understanding the need for cutting edge solution techniques to organizational decisions. A greater management concern is to have techniques and practical models that can evaluate their operations and make decisions that are not only optimal but also consistent with the changing environment. Motivated by the myriad need to mitigate the risk of uncertainties in performance evaluations, this thesis focuses on finding robust and flexible evaluation strategies to the ranking and classification of DMUs. It studies performance measurement with the DEA tool and addresses the uncertainties in data via the robust optimization technique. The thesis develops new models in robust data envelopment analysis with applications to management science, which are pursued in four research thrust. In the first thrust, a robust counterpart optimization with nonnegative decision variables is proposed which is then used to formulate new budget of uncertainty-based robust DEA models. The proposed model is shown to save the computational cost for robust optimization solutions to operations research problems involving only positive decision variables. The second research thrust studies the duality relations of models within the worst-case and best-case approach in the input – output orientation framework. A key contribution is the design of a classification scheme that utilizes the conservativeness and the risk preference of the decision maker. In the third thrust, a new robust DEA model based on ellipsoidal uncertainty sets is proposed which is further extended to the additive model and compared with imprecise additive models. The final thrust study the modelling techniques including goal programming, robust optimization and data envelopment to a transportation problem where the concern is on the efficiency of the transport network, uncertainties in the demand and supply of goods and a compromising solution to multiple conflicting objectives of the decision maker. Several numerical examples and real-world applications are made to explore and demonstrate the applicability of the developed models and their essence to management decisions. Applications such as the robust evaluation of banking efficiency in Europe and in particular Germany and Italy are made. Considering the proposed models and their applications, efficiency analysis explored in this research will correspond to the practical framework of industrial and organizational decision making and will further advance the course of robust management decisions

Box-Jenkins modelling and forecasting of Brent crude oil price

The volatility in the crude oil price in the international market has risen much interest into the investigation of its price swing. In this project, we examine the dynamics of the monthly Brent oil price for the last two decades using the Box Jenkins ARIMA techniques and show that such model is not able to capture the volatility inherent in the crude oil price for an accurate forecast. We first divided the data into two. The first seventeen years used for the model construction and the last three years validating forecasting accuracy. The data is first differenced for stationarity and autocorrelation and residuals techniques used to select different ARIMA models for analysis. The performance of different models were compared and the result shows that a non-parsimonious ARIMA (1,1,1) model was the best forecasting model amidst the volatilities in the oil price

Box-Jenkins modelling and forecasting of Brent crude oil price

The volatility in the crude oil price in the international market has risen much interest into the investigation of its price swing. In this project, we examine the dynamics of the monthly Brent oil price for the last two decades using the Box Jenkins ARIMA techniques and show that such model is not able to capture the volatility inherent in the crude oil price for an accurate forecast. We first divided the data into two. The first seventeen years used for the model construction and the last three years validating forecasting accuracy. The data is first differenced for stationarity and autocorrelation and residuals techniques used to select different ARIMA models for analysis. The performance of different models were compared and the result shows that a non-parsimonious ARIMA (1,1,1) model was the best forecasting model amidst the volatilities in the oil price

Entrepreneurial opportunity decisions under uncertainty:Recognizing the complementing role of personality traits and cognitive skills

Purpose: The aim of this paper focuses on advancing the entrepreneurial literature by enhancing the understanding of the connections between personal behavior and cognitive skills in decision making under uncertainty. Methodology: The method of this research has been adapted the framework used by Garrett andHolland (2015), who developed propositions from the conceptual narratives of how environmental uncertainty and complexity differentially affect the motivations and cognition of independent entrepreneurs and corporate entrepreneurs to engage inentrepreneurial action. Findings: The findings of this research provide a conceptual basis for a broader perspective on behaviors and cognitions that motivate or hinder entrepreneurial actions while at the same time, positioning the entrepreneur’s decision at the core of decision theory. Implications for theory and practice:Theoretically, this research contributes to a holistic view of opportunity decisions. It redirects the traditional analyses path of entrepreneurial decisions discussed distinctively from the personal behavior or cognition paradigm, which does not provide a complete view into the larger entrepreneurial decisions under uncertainty.Practically, our argument provides further insight into the black box of entrepreneurial opportunity decisions under uncertainty and thus highlights the need for a broader perspective for the entrepreneur, especially in the early stage of venture formation,where some cognitions and required personal attributes are needed in consonance for entrepreneurial action. Originality and value: Entrepreneurship research on decision making under uncertainty has mainly focused on the effect of uncertainty on entrepreneurial actions, while an attempt at the individual level, particularly, fromthe cognitive framework seeks to explain why actions differ. Scholarly efforts have also been made on what informs entrepreneurial actions from the perspective of the entrepreneur’s personal attributes. However, no integrated approach is offered in the literature to study how cognitive skills and personality traits complement each other. <br/

Hydrological and Physical Changes of Soils Under Cocoa Plantations of Different Ages During the Dry Season in the Transition Zone of Ghana

The study was conducted at the University of Education, Winneba, Mampong Campus from October, 2007 to March, 2008 to assess the hydrological and physical changes that take place in soils under cocoa plantations of different ages as climate changed through the dry season. The treatments were T1 (control, representing an adjoining grass fallow land), T2 (5-year old cocoa plantation), T3 (4-year old cocoa plantation) and T4 (3-year old cocoa plantation). The parameters measured were: Bulk density, Total porosity, organic matter, gravimetric moisture content, potential evaporation and Net Litter Accumulation (NLA) of the plants. From the results, T2 recorded the highest gravimetric moisture content, porosity, litter fall, organic matter and lowest bulk density and potential evaporation. T1 recorded the lowest and highest values for gravimetric moisture content (3.5%) and evaporation loss of water (249.0mm), respectively, at day 84. Correlation analysis revealed that soil moisture was highly influenced by bulk density, total porosity, potential evaporation and net leaf litter accumulation. Soil moisture storage negatively correlated with potential evaporation (r = -0.987) and bulk density (r = -0.985) but positively correlated with Total porosity (r =0.984) and net litter accumulation (r = 0.941). The proper manipulation of these parameters would ensure good soil moisture retention and better adaptations of cocoa to unfavourable conditions driven by climate change in the Transition Zone of Ghana. Keywords: Gravimetric moisture, potential evaporation, porosity, leaf litter, correlatio

Computed tomography features of spontaneous acute intracranial hemorrhages in a tertiary hospital in Southern Ghana

Introduction:&nbsp;spontaneous acute intracranial hemorrhage (SICH) accounts for approximately 10-15% of all stroke cases. Early detection by computed tomography (CT) and early treatment are key. Hence this study to examine the CT features of SICH. Methods:&nbsp;this retrospective cohort study reviewed all 435 patients diagnosed with SICH from 1st&nbsp;March, 2017 to 1st&nbsp;January, 2021 in a tertiary facility in Southern Ghana. Data collected (age, sex, SICH type and the CT scan features) were organized and analyzed using GNU PSPP and Libre Office Calc. Statistical significance level was pegged at p≤0.05. Results:&nbsp;the SICH types were acute intracerebral hemorrhage (97.93%), acute subarachnoid/intraventricular hemorrhage (1.15%), acute epidural hemorrhage (0.46%) and acute subdural hemorrhage (0.46%). Acute intracerebral hemorrhage was more common in those &gt;60 years (57.75%, p&lt;0.001). The commonest CT feature for acute intracerebral hemorrhage was hyperdense lesion with perilesional edema (40.98%), with smoking (OR=2.24, 95% CI: 1.14-4.41, p=0.019) and anticoagulants intake (OR=2.56, 95% CI: 1.15-5.72, p=0.022) as the predictive factors; followed by hyperdense lesion extending to the edge of the brain (25.03%), also predictable by smoking (OR=0.23, 95% CI: 0.11-0.47, p&lt;0.001); and hyperdense lesion with mass effects (22.70%) was not predictive with any risk factor. Type 2 diabetes mellitus (60.00%, p&lt;0.001) and smoking (97.83%, p&lt;0.001) were more common in males. Conclusion:&nbsp;hyperdense lesion with perilesional edema was the most frequent CT feature for acute intracerebral hemorrhage and was predictable by smoking and anticoagulants intake. Smoking was a predictive factor to the development of most of the features of acute intracerebral hemorrhage

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London