Finding Targets in Non-Radial FDH Models: A Hybrid Technique Based on STEM and Extended Ratio Based Approach

Performance evaluation using interactive methods and extended ratio-based approaches can be very important for some organizations. Free disposal hull models can be created if there is no concern for convexity, and using non-radial DEA models can simultaneously create more logical and practical situations for finding DMU targets. In this paper, a new hybrid technique based on the additive slack based method and enhanced Russel measure in variable return to scale technology has been proposed. The proposed technique can find decision making unit targets in non-radial free disposal hull models using the step method. Furthermore, the extended ratio-based approach in the proposed technique has been applied to find DMU targets of related non-radial free disposal hull models without solving any mathematical programming models. Finally, targets of Fars province pharmaceutical distributing companies were found by applying the proposed hybrid technique.

The impact of ownership and size heterogeneity on hotel efficiency in the Canary Islands (Spain)

This study was conducted to analyse the influence of technological differences on hotel efficiency in the Canary Islands (Spain), with particular regard to the heterogeneity observed in hotel ownership and size. A metafrontier approach, based on non-parametric deterministic efficiency methods (data envelopment analysis and free-disposal hull) and robust non-parametric estimators (order-α), is used. This empirical analysis considered a panel data sample selection model of Canary Islands hotels for the period 2002–2015. The results obtained show that the frontiers against which the hotels are compared (metafrontier or group) and the consideration or otherwise of outliers are factors of crucial importance. We find that efficiency depends on hotel size (large hotels are more efficient than small ones), but not on the type of ownership. The results also show that the impact of the global financial crisis on the average technical efficiency of these hotels was slight or non-existent. Finally, the technological gap narrowed over time, especially in large hotels and those with no majority shareholder.

Analysis of Efficiency for Zakat Management Organization in Indonesia: A Comparison Study of Super Efficiency and Free Disposal Hull

This research aims to analyze the efficiency level of fourteen Zakat Management Organizations (ZMO) in Indonesia. This study uses a quantitative approach with the method of Free Disposal Hull (FDH) and the Super Efficiency (SE) method. Socialization Expense, Operational Expense, and Salary Expense are the input variables, while zakah fund collection and zakah distribution become the output variables. Empirical findings show that ZMO Corp 4 has the highest efficiency level, which analyzed through both methods of FDH or SE. ZMO managed by the government is the most efficient ZMO compared to the others who managed by private group and social organization. Total Potential Importance (TPI) shows that the output variable that requires the most significant adjustment is the zakat distribution, which is 12.66%

Dual frontiers data envelopment analysis: multiple ranking approaches from optimistic and pessimistic perspectives

The field of data envelopment analysis (DEA) has evolved rapidly since its introduction to decision-making science 40 years ago. DEA has since attracted the attention of many researchers because of its unique characteristic to measure the efficiency of multiple-input and multiple-output decision-making units (DMUs) without assigning prior weight to the input and output, unlike most available decision analysis tools. The body of research has resulted in a huge amount of literature and diverse DEA models with very many different approaches. DEA classifies all units under assessment into two groups: efficient with a 100% efficiency score and inefficient with a less than 100% efficiency score. This ability is considered both a strength and a weakness of the standard DEA model because, although it allows DEA to evaluate the efficiency of any dataset, it lacks the power to rank all DMUs, by giving full efficiency scores to many efficient units. This issue has attracted many researchers to investigate the weak discrimination power of classical DEA models, resulting in a subfield of research that focuses on DEA ranking. This thesis focuses on the development of the conventional DEA model, and an attempt has been made to study models that are considered as improved models, or approaches that bring a better ranking field, that may bring more accurate evaluation than the original DEA. After studying DEA ranking models, the thesis presents various models under the optimistic and pessimistic DEA ranking approaches. The first and fundamental contribution are the optimistic and pessimistic free disposal hull (FDH) models. In this study, authentic optimistic and pessimistic DEA models without convexity are developed from both input and output orientation. Further into the research investigation, extended models have been proposed, by combining the conventional and FDH ranking models with other different approaches in the literature. Chapter 4 of this thesis presents three extended FDH models: an FDH slack-based model, an FDH superefficiency model, and a dual frontier without infeasibility super-efficiency FDH model. Chapter 5 shows the development of extended models when virtual DMUs are considered. Improved virtual DMU models and improved FDH virtual DMU models are proposed in order to develop the DEA ranking ability from both optimistic and pessimistic approaches. The final model is an optimistic and pessimistic forecasting approach using regression analysis. The forecasting model can be used by decision makers to determine the resources needed for future planning to build an efficient new unit with reference to the current DMU set.

Dual frontiers data envelopment analysis: multiple ranking approaches from optimistic and pessimistic perspectives

The field of data envelopment analysis (DEA) has evolved rapidly since its introduction to decision-making science 40 years ago. DEA has since attracted the attention of many researchers because of its unique characteristic to measure the efficiency of multiple-input and multiple-output decision-making units (DMUs) without assigning prior weight to the input and output, unlike most available decision analysis tools. The body of research has resulted in a huge amount of literature and diverse DEA models with very many different approaches. DEA classifies all units under assessment into two groups: efficient with a 100% efficiency score and inefficient with a less than 100% efficiency score. This ability is considered both a strength and a weakness of the standard DEA model because, although it allows DEA to evaluate the efficiency of any dataset, it lacks the power to rank all DMUs, by giving full efficiency scores to many efficient units. This issue has attracted many researchers to investigate the weak discrimination power of classical DEA models, resulting in a subfield of research that focuses on DEA ranking. This thesis focuses on the development of the conventional DEA model, and an attempt has been made to study models that are considered as improved models, or approaches that bring a better ranking field, that may bring more accurate evaluation than the original DEA. After studying DEA ranking models, the thesis presents various models under the optimistic and pessimistic DEA ranking approaches. The first and fundamental contribution are the optimistic and pessimistic free disposal hull (FDH) models. In this study, authentic optimistic and pessimistic DEA models without convexity are developed from both input and output orientation. Further into the research investigation, extended models have been proposed, by combining the conventional and FDH ranking models with other different approaches in the literature. Chapter 4 of this thesis presents three extended FDH models: an FDH slack-based model, an FDH superefficiency model, and a dual frontier without infeasibility super-efficiency FDH model. Chapter 5 shows the development of extended models when virtual DMUs are considered. Improved virtual DMU models and improved FDH virtual DMU models are proposed in order to develop the DEA ranking ability from both optimistic and pessimistic approaches. The final model is an optimistic and pessimistic forecasting approach using regression analysis. The forecasting model can be used by decision makers to determine the resources needed for future planning to build an efficient new unit with reference to the current DMU set.

The Importance of Well-Being on Resiliency of Filipino Adults During the COVID-19 Enhanced Community Quarantine: A Necessary Condition Analysis

Nation-wide community quarantines and social distancing are part of the new normal because of the global COVID-19 pandemic. Since extensive and prolonged lockdowns are relatively novel experiences, not much is known about the well-being of individuals in such extreme situations. This research effort investigated the relationship between well-being elements and resiliency of 533 Filipino adults who were placed under the nationwide enhanced community quarantine (ECQ) during the COVID-19 pandemic. Participants comprised of 376 females (70.56%) and 157 males (29.45%). The median and mode ages of the participants is 23 years, while 25 is the mean age. PERMA Profiler was used to measure participants’ well-being elements, while Connor-Davidson Resiliency Scale-10 (CD-RISC-10) was used to measure their resiliency. Collected data were analyzed using the regression model and necessary condition analysis. This study corroborated that all the five pillars of well-being are significant positive correlates of resiliency (p < 0.00) in quarantined adults. The results shown accomplishment (β = 0.447, p < 0.01) positively predicts resiliency, while negative emotions (β = −0.171, p < 0.00) negatively predict resiliency. Lastly, the five pillars of well-being are necessary-but-not-sufficient conditions (ceiling envelopment with free disposal hull, CE-FDH p < 0.00) of resiliency. Our results cast a new light on well-being elements as constraints rather than enablers of resiliency. This novel result shows that optimum resiliency is only possible when all the five pillars of well-being are taken care of and when a person is at least minimally contented with their physical health. The present findings underscore the importance of a holistic as against an atomistic approach to maintaining good mental health, which suggests that deficiencies in certain areas of well-being may not be fully addressed by overcompensating on other areas, as all five pillars of well-being are necessary-but-not-sufficient conditions of resiliency. The study ends with the recommendation for the use of necessary condition analysis to study both classical and novel psychological research problems.

Order Picking and E-Commerce: Introducing Non-Parametric Efficiency Measurement for Sustainable Retail Logistics

Order picking is a crucial but labor- and cost-intensive activity in the retail logistics and e-commerce domain. Comprehensive changes are implemented in this field due to new technologies like AI and automation. Nevertheless, human worker’s activities will be required for quite some time in the future. This fosters the necessity of evaluating manual picker-to-part operations. We apply the non-parametric Data Envelopment Analysis (DEA) to evaluate the efficiency of n = 23 order pickers processing 6109 batches with 865,410 stock keeping units (SKUs). We use distance per location, picks per location, as well as volume per SKU as inputs and picks per hour as output. As the convexity axiom of standard DEA models cannot be fully satisfied when using ratio measures with different denominators, we apply the Free Disposal Hull (FDH) approach that does not assume convexity. Validating the efficiency scores with the company’s efficiency assessment, operationalized by premium payments shows a 93% goodness=of-fit for the proposed model. The formulated non-parametric approach and its empirical application are promising ways forward in implementing empirical efficiency measurements for order picking operations within e-commerce operations.

The Influence of Excellence on Municipal Performance: Quasi-Experimental Evidence From the Czech Republic

This article examines the impact of excellence-promoting policies on the actual performance of municipalities in the Czech Republic. In this analysis, the performance of municipalities that have received awards for the use of quality management tools is compared with a selected group of municipalities that did not receive awards. Data envelopment analysis (DEA; with constant and variable returns on the scale), free disposal hull (FDH), and Order-M methods were utilized to represent performance. For the actual performance comparison, a quasi-experimental design was used. The analysis of outputs using the difference impact method found that this specific public policy did not have a positive impact on the efficiency of municipalities. If the difference-in-differences method is used, the opposite is achieved. However, the technical efficiency gains are very small. The use of the quasi-experimental design along with the determination of inputs and outputs which are characteristic of the Czech Republic also offers a contribution when this method is being applied to the assessment of institutions in the form of local governments.

A Novel Machine Learning Approach Combined with Optimization Models for Eco-efficiency Evaluation

Machine learning approaches have been developed rapidly and also they have been involved in many academic findings and discoveries. Additionally, they are widely assessed in numerous industries such as cement companies. Cement companies in developing countries, despite many profits such as valuable mines, face many challenges. Optimization, as a key part of machine learning, has attracted more attention. The main purpose of this paper is to combine a novel Data Envelopment Analysis (DEA) approach in optimization at the first step to find the Decision-Making Unit (DMU) with innovative clustering algorithms in machine learning at the second step introduce the model and algorithm with higher accuracy. At the optimization section with converting two-stage to a simple standard single-stage model, 24 cement companies from five developing countries over 2014–2019 are compared. Window-DEA analysis is used since it leads to increase judgment on the consequences, mainly when applied to small samples followed by allowing year-by-year comparisons of the results. Applying window analysis can be beneficial for managers to expand their comparison and evaluation. To find the most accurate model CCR (Charnes, Cooper and Rhodes model), BBC (Banker, Charnes and Cooper model) and Free Disposal Hull (FDH) DEA model for measuring the efficiency of decision processes are used. FDH model allows the free disposability to construct the production possibility set. At the machine learning section, a novel three-layers data mining filtering pre-processes proposed by expert judgment for clustering algorithms to increase the accuracy and to eliminate unrelated attributes and data. Finally, the most efficient company, best performance model and the most accurate algorithm are introduced. The results indicate that the 22nd company has the highest efficiency score with an efficiency score of 1 for all years. FDH model has the highest efficiency scores during all periods compared with other suggested models. K-means algorithm receives the highest accuracy in all three suggested filtering layers. The BCC and CCR models have the second and third places, respectively. The hierarchical clustering and density-based clustering algorithms have the second and third places, correspondingly.