Abstract. Data Envelopment Analysis is a popular tool for assessing the relative efficiency of units, treated as a whole unit, without considering the internal structure of decision making units. In contrast to the conventional models, Network DEA models consider the internal operations of units. In both models, the most favourable weights of the input-output factors for each unit are used. One of the main pitfalls of these models is that they require a set of n different vectors of these weights for the units, thus, the factors may not have the same importance for each unit. To overcome this drawback and avoid ignoring some factors, several methods have been proposed to determine a common set of weights (CSWs) of the factors. However, none of the proposed approaches can determine the CSWs in network DEA models. This paper contributes to the existing literature by proposing a novel approach for determining the CSWs based on Entropy Method. Finally, the proposed approach is applied to demonstrate their applicability in both numerical and illustration examples. Moreover, a case of assessment of Science and Technology Parks in Turkey was used for the purpose of illustration.
Abstract. Aviation is strategically important because of its enormous contribution to the economy, as well as being the field of application and production of new technologies. The efficiency of airports comes to the fore in efficiency researches in aviation. However, efficiency analyzes with Data Envelopment Analysis (DEA) using single-year data may not be sufficient to determine the trend. Therefore, output-oriented Window-DEA (W-DEA) using multi periods was performed in this study. Moreover, W-DEA provides information about the increases and decreases in the efficiency trends of airports. The research was carried out with the data of 42 Turkish airports for the period 2014-2019. The findings show that two airports in Istanbul as well as Adana, Van and Antalya airports have high efficiency. With this study, measuring the performance of Turkish airports with W-DEA has been brought to the literature. This study will shed light on the feasibility studies of new investments as well as the sustainability and competitiveness studies.
Abstract. The young population is quite high as the birth rate in our country is higher than in other countries. The high number of young people increases the demand for higher education and as a result of this demand, universities gain importance. Thus, the number of universities constituting the educational infrastructure of our country has increased rapidly in recent years. Therefore, it caused that increased the number of newly opened departments and quotas depending on the number of universities. In the face of rapid growth, public investment demands of universities to solve the infrastructure problem have also increased. Despite the establishment of new buildings and classrooms built in the campuses created to fill the quotas, 71 thousand quotas remained empty in 2019. Thus, public resources were used inefficiently due to the fact that the envisaged public investments could not be utilized to the maximum. In this study, the places of the 125 state universities in Turkey were analyzed whether used efficiently. The number of buildings, the number of campuses, and the size of the indoor space they used were taken into consideration for this purpose. In addition, universities were grouped and evaluated according to the provinces where they were located and the years they were founded. While there was no significant difference between the groups according to CCR scores, it was concluded that there was a significant difference between the groups according to BCC scores. The test results show that there is a 5% significance difference between 32 universities established between 2006-2007 and 27 universities established until 1982 and 26 universities established between 1987-1994. The findings show that most of the universities allocated in 2018 use public resources efficiently. Furthermore, this study provides recommendations to decision-makers in evaluating new public investment demands.
Abstract. Ranking of efficient decision-making units (DMUs) allows a further analysis of the data envelopment analysis (DEA) efficient DMUs, where otherwise a high number of efficient DMUs can be identified. The current research seeks to segregate the efficient DMUs through the use of seven cross-efficiency (CE) ranking approaches. The choice of using those CE approaches was meant to reduce bias associated with the use of a single criterion. The seven approaches were extended to suit a situation were DMUs are structured in a two-level hierarchical grouping. The CE of level 1 DMUs was adjusted with the performance of the level 2 DMUs. Variations in the seven adjusted CE ratings were observed. In a bid to ensure complete ranking, the technique for order preference by similarity to ideal solution (TOPSIS) ranking method was combined with the adjusted CE ratings. The results from a real-world example revealed complete ranking for the majority of DMUs.
Abstract. Purpose: Covid-19 pandemic spread rapidly around the world and required strict restriction plans and policies. In most countries around the world, the outbreak of the disease has been serious and has greatly affected the health system and the economy. The factors such as the number of patients with chronic diseases, the number of people over 65 years old, hospital facilities, the number of confirmed Covid-19 cases, the recovering Covid-19 cases and the number of deaths affect the rate of spread of Covid-19. This study aims to evaluate the performances of 21 OECD (Organisation for Economic Co-operation and Development) countries against the Covid-19 outbreak using three Data Envelopment Analysis (DEA) models.
Design/methodology/approach: In this study, the performance of 21 OECD countries to manage the Covid-19 process has been analysed weekly via DEA which is widely used in various practical problems and provides a general framework for efficiency evaluation problems using the inputs and outputs of decision-making units.
Findings: The analysis showed that 11 countries out of 21 countries were efficient for selected weeks. According to the DEA results from the twenty-week review (09.04.2020-20.08.2020), information about the course of the epidemic prevention and the normalization process for any country can be obtained.
Originality/value: For the first time in literature, Covid-19 management efficiency is analysed using the cross-efficiency and super-efficiency model proposed to solve this problem the problem of the discrimination power of DEA.
Abstract. This paper explores the structural and operational dimensions of the efficiencies of airports. The two-stage procedure is suggested to assess the efficiencies of airports in this study. In the first-stage, Classification and Regression Tree, which is one of the machine-learning approaches used to divide the airports into homogeneous and thus comparable sub-groups. In the second stage, the bootstrap data envelopment analysis approach obtains more precise structural and operational efficiency scores. To illustrate the proposed framework use, we applied it to a real case associated with Turkish airports. The results demonstrate that this framework presents a more comprehensive assessment of airport performance rather than conventional data envelopment analysis models. Moreover, it provides to show the deficiencies of the structural and operational management of airports. The findings can help anywhere airport authorities as well as Turkish airport authorities.
Abstract. Data envelopment analysis (DEA) is a very effective management tool in assessing the performance of a set of decision making units (DMUs). In the efficiency evaluation using classic single stage DEA models, the internal processes of the DMUs are often neglected. In most real-world problems, it may be more realistic to evaluate the efficiency evaluation in two-stage production systems. In some cases: however, the decision-maker must need to identify the most efficient single unit. Numerous methods have been introduced to find the most efficient unit in single stage systems whereas no methods have been proposed for this aim in two stage production systems. Therefore, a new model based on mixed-integer programming was proposed to determine the most efficient DMU in two-stage systems and sub-stages in this study. The most important innovation of the suggested approach is that the most efficient DMUs of both stages can be found separately using only one model. Numerical examples for real world problems and a simulation study were provided for the validity of the proposed model.
Abstract. Purpose : This paper aims to consider each strategy of the particle swarm optimization (PSO) as a unit in data envelopment analysis (DEA) and uses the minimax mixed-integer linear programming DEA approach to find the most suitable inertia weight strategy. A total of 15 inertia weight strategies were empirically examined in a suite of 42 benchmark problems in the view of DEA.
Design/methodology/approach :PSO is very sensitive to inertia weight strategies, and therefore, an important amount of research attempts has been concentrated on these strategies. There is no research into the determination of the most suitable inertia weight strategy; however, there are a large number of comparisons related to the inertia weight strategies. DEA is one of the performance evaluation methods, and its models classify the set of strategies into two distinct sets as efficient and inefficient. However, only one of the strategies should be used in the PSO algorithm. Some effective models were proposed to find the most efficient strategy.
Findings : The experimental studies demonstrate that an approach is a useful tool in the determination of the most suitable strategy. Besides, if the author encounters a new complex problem whose properties are known, it will help the author to choose the best strategy.
Practical implications : A heavy oil thermal cracking three lumps model for the simplification of the reaction system was used because it is an important complicated chemical process. In addition, the soil water retention curve (SWRC) plays an important role in diverse facets of agricultural engineering. As the SWRC can be regarded as a nonlinear function between the water content and the soil water potential, Van Genuchten model is proposed to describe this function. To determinate these model parameters, an optimization problem is formulated, which minimizes the difference between the measured and modeled data.
Originality/value : In this paper, the PSO algorithm is integrated with minimax mixed-integer linear programming to find the most suitable inertia weight strategy. In this way, the best strategy could be chosen for a new more complex problem.
Abstract. Data envelopment analysis is a very effective mathematical instrument in assessing the performance of decision-making units. In most real cases, the decision-maker need to identify a single most efficient unit. Several approaches were proposed for this necessity in the literature using data envelopment analysis. This study, based on the two steps model suggested by Toloo and Salahi (2018), proposes a new model without epsilon to choose the most efficient unit. The proposed model has fewer constraints than their model and is solved by a one-step linear programming model without epsilon. The proposed model determines exactly one DMU as the most efficient one and other decision-making units have efficiency scores strictly less than one. A simulation study was designed to test the proposed model in terms of some criteria such as correlation. In addition, the examples of real cases whose real rank is known and frequently used in literature of the most efficient unit were preferred for the validity of the proposed model. The results illustrated that the discrimination power problem was experienced in the previous models whereas no such problem was observed in the new proposed model for the same real cases.
Abstract. Background : Recently, appointment registration systems (especially phone and web-based) that provide easier access to hospital medical services have become more common than traditional queuing registration systems. Therefore, this study aimed to evaluate various appointment registration systems called the Central Physician Appointment System (CPAS) in Turkish public hospitals.
Methods : Data obtained from the Health Ministry of Turkey for 2013 to 2018 were analysed to measure patients' use of the CPAS using several indicators.
Results : In total, 22.1% of patients used the appointment system for registering in 2013, and this rose to 34.6% in 2018. The Hello 182 call centre was used for 60.8% of appointments that were made, whereas patients used other systems less (39.2%). Females accounted for 62.2% of patients who used appointment systems to register. It was observed that the appointment density at training and research hospitals and oral-dental health centres/hospitals was high. Over time, both the waiting time before consultation and the total time at the hospital have decreased.
Conclusions : Despite significant gains in the efficiency of outpatient services, effective functioning of the CPAS is not sustainable in Turkey because of the current health policies. Therefore, in order for the CPAS to function effectively, either political measures should be taken such as the creation of a mandatory referral chain or the CPAS should be reorganized based on supply-demand.
Abstract. The generalized gamma distribution (GGD) is a popular distribution because it is extremely flexible. Due to the density function structure of GGD, estimating the parameters of the GGD family by statistical point estimation techniques is a complicated task. In other words, for the parameter estimation, the maximizing likelihood function of GGD is a problematic case. Hence, alternative approaches can be used to obtain estimators of GGD parameters. This paper proposes an alternative parameter estimation method for GGD by using the heuristic optimization approaches such as Genetic Algorithms (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). A comparison between different modern heuristic optimization methods applied to maximize the likelihood function for parameter estimation is presented in this paper. The paper also investigates both the performance of heuristic methods and estimation of GGD parameters. Simulations show that heuristic approaches provide quite accurate estimates. In most of the cases, DE has better performance than other heuristics in terms of bias values of parameter estimations. Besides, the usefulness of an alternative parameter estimation method for GGD using the heuristic optimization approach is illustrated with a real data set.
Abstract. The ranking of the decision making units (DMUs) is an essential problem in data envelopment analysis (DEA). Numerous approaches have been proposed for fully ranking of units. Majority of these methods consider DMUs with optimistic approach, whereas their weaknesses are ignored. In this study, for fully ranking of the units, a modified optimistic–pessimistic approach, which is based on game cross efficiency idea is proposed. The proposed game like iterative optimistic-pessimistic DEA procedure calculates the efficiency scores according to weaknesses and strengths of units and is based on non-cooperative game. This study extends the optimistic-pessimistic DEA approach to obtain robust rank values for DMUs. The proposed approach yields Nash equilibrium solution, thus overcomes the problem of non-uniqueness of the DEA optimal weights that can possibly reduce the usefulness of cross efficiency. Finally, in order to verify the validity of the proposed model and to show the practicability of algorithm, we apply a real-world example for selection of industrial R&D projects. The proposed model can increase the discriminating power of DMUs and can fully rank the DMUs.
Abstract. Supplier selection is one of the most important strategic issues in supply chain management and plays a key role in an organization. Selecting a suitable supplier is one of the most important decisions of the purchasing department, and this decision is usually based on various criteria. In this study, a mixed-integer data envelopment analysis model is proposed to evaluate and select the best (most efficient) supplier with input, intermediate and output in a supply chain structure expressed as a two-stage production process. The proposed model was applied to the problem of selecting the suppliers of both the plastic packing strap industry and the suppliers of the resin manufacturers and the performance of the model was compared with the other studies. The results show that the proposed mixed-integer two-stage model facilitates the decision-maker's choice of supplier.
Abstract. Background : Turkish public hospitals have been subjected to health care reform because of increasing cost pressure, inequities in access to health care, poor quality of care and limited patient responsiveness in the last three decades. This study investigates the impact of recent hospital reforms on the efficiency of public hospitals.
Methods : The study provides a comprehensive evaluation of the efficiency of Turkish hospitals by using Data Envelopment Analysis (DEA). The estimation of efficiency of 669 public hospitals of Turkey is performed by an output-oriented model of DEA under the assumption of variable return-to-scale by using data collected from the Ministry of Health (MoH) over the period 2013–17.
Results : The average efficiency score is equal to 0.83 for all MoH hospitals. Considering the hospital type, the efficiency scores of training and research hospitals are higher than those of the general and branch hospitals. In addition, considering the hospital size, huge-scale hospitals have the highest efficiency score in all years. Moreover, overcrowded regions such as Marmara and South-eastern Anatolia regions had higher efficiency scores than other geographical regions.
Conclusions : The results indicate that recent health reforms did not significantly enhance hospital efficiency. Thus, policymakers and managers should take the necessary precautions to increase hospital efficiency.
Abstract. A total of 27 accessions of different onion landraces from Turkey were screened for their resistance to stem and bulb nematode. The study was carried out in a growth chamber at 20°C, with a 16/8 h (light/dark) photoperiod and at 70% relative humidity. The plants were grown in 7 X 8 cm diameter plastic pots filled with a mixture of 45% sand, 45% clay loam soil, 10% organic matter. Two hundred nematodes in 10 µl nematode suspension were inoculated to each plant at the 3-4 leaves stage. Plants were harvested six weeks after inoculation and number of nematodes was counted. Onion landraces that had low nematode reproduction were subjected to a second screening test. The landraces were classified from moderately resistant to highly susceptible according to their nematode reproduction in comparison to susceptible standard cultivar Betapanko. Accession 30 had the lowest number of nematodes in the both experiments and was classified moderately resistant and moderately susceptible in the first and second experiments, respectively. The accessions 23 and 25 had lower number of nematodes relative to standard cultivar and were classified as moderately susceptible in conclusion.
Abstract. Cross efficiency evaluation in data envelopment analysis (DEA) is an effective tool for ranking the performance of decision-making units (DMUs). Numerous cross efficiency evaluations have been proposed with different secondary goals using both peer-evaluation and self-evaluation. The neutral cross efficiency evaluation is an important secondary goal in the classical black-boxes DEA models; that is, the internal processes of the DMUs are often ignored in the efficiency evaluation. This study extends the idea of neutral cross evaluation to measure the efficiency of the basic two stage network systems and proposes a new neutral cross efficiency model. The proposed model is able to rank DMUs in sub-stages and decompose the cross efficiency measure of the system into the product of those of the stages. The results from two real-world examples show that the neutral cross efficiency model proposed in this paper can increase the discriminating power of a two-stage system and their sub-stages and can obtain more realistic weight scheme than basic two stage DEA model.
Abstract. The distribution of plant-feeding and free-living nematodes in large scale onion production areas in five geographical regions in Turkey was investigated in 2016 and 2017. Ditylenchus spp. and Tylenchus spp. were widely distributed. The stem and bulb nematode, Ditylenchus dipsaci, was found in 48 locations from 13 provinces. Other plant-feeding nematode genera were Pratylenchus, Paratylenchus and Pratylenchoides. Pratylenchus thornei was the most widely distributed root-lesion nematode species in onion fields in 11 locations from seven provinces. Pratylenchus neglectus was present in three locations and P. vulnus was in four locations. Aphelenchus spp. and Aphelenchoides spp. were the principal fungal-feeding nematodes in onion-growing areas. The most abundant bacterial-feeding nematode genera were Acrobeloides, Cephalobus, Eucephalobus and Rhabditis. Acrobeles and Wilsonema genera were low in occurrence and abundance. Nematodes from Dorylaimida and predator nematodes, Mononchus spp., were also found. The numbers of Ditylenchus from plant samples were significantly correlated positively to silt content, and significantly correlated negatively to organic matter and calcium content.
Abstract. The performance of the units is defined as the ratio of the weighted sum of outputs to the weighted sum of inputs. These weights can be determined by data envelopment analysis (DEA) models. The inputs and outputs of the related (Decision Making Unit) DMU are assessed by a set of the weights obtained via DEA for each DMU. In addition, the weights are not generally common, but rather, they are very close to zero or they are even equal to zero. This means that some major criteria will not be considered. Another problem is the similarity of the efficiency scores of efficient DMUs. However, this is not the case in reality, and the performance of the DMUs should be completely ranked. Using common weights can solve these problems completely during measuring the performance of DMUs. There are some articles in the literature to determine common weight sets (CSWs), but none of them takes into account the bootstrap approach. This paper introduces a novel, empirical and robust algorithm based on bootstrapping technique to find CSWs.
Abstract. The Burr III distribution is a very popular distribution for modeling real data in terms of risk, reliability, and process capability, and thus the estimation of its parameters is essential in most real applications. The classical estimation methods, such as maximum likelihood and least squares, are often used to estimate the parameters of the Burr III distribution. However, maximizing the likelihood function developed for the parameter estimation of the four-parameter Burr type III distribution is a quite difficult problem. Hence, the heuristic approaches must be used to discover good solutions. Particle swarm optimization (PSO) is one of the heuristic approaches, which is a population-based technique developed from swarm intelligence. This paper proposes an alternative parameter estimation method for Burr III distribution using the PSO heuristic approach. Simulation results show that the PSO approach provides accurate estimates and the PSO method is satisfactory for the parameter estimation of Burr III distribution.
Abstract. Football is a very popular subdivision of sports for our country, as all around the world and the money spent for football is on a large scale. In spite of this, scientific studies interested in the statistical, economical dimensions and performance evaluation for the football are scarce in our country even in the world. One of the reasons of this is not to record football statistics in our country. FIFA statistics can be hold as a sample in this field. FIFA, holds about fifty numbers of different football statistics at player basis or team level during the World Cup matches. It is obvious that the performance of players one by one and as an entire and the team’s performance is so important. The goal of this study is to pioneer for how the performance analysis is applied and how its results can be benefited in football if the statistics are recorded. Displaying the numerical size of defectiveness mathematically also helps trainers and players. On the other hand, the output oriented CCR model of data envelopment analysis and super efficiency model AP were used.
Abstract. Nonlinear regression models are widely used for modeling of stochastic phenomena and the estimating parameters problem plays a central role in the inference in nonlinear regression models. In this paper, this problem has been briefly discussed and an effective approach based on the Particle Swarm Optimization (PSO) algorithm is proposed in order to enhance the estimation accuracy. The PSO algorithm is tested on the well-known 28 nonlinear regression tasks of various level of difficulty. The results show that PSO approach which exhibits a rapid convergence to the minimum value of the sum of squared error function in less iterations, provides accurate estimates and is satisfactory for the parameter estimation of the nonlinear regression models
Abstract. Data Envelopment Analysis (DEA) is non-parametric mathematical tool a linear programming-based approach for measuring the relative efficiency of decision makin gunits (DMUs). DEA is becoming widely used to evaluate the efficiency of organizations with multiple homogeneous DMUs such as universities, hospitals, and banks that produce several outputs with a variety of inputs. Different model selection methods have been suggested for DEA in the literature. Model selection in DEA is a very important problem. Efficiency score of DMU takes different values based on input and output. Variable selection is crucial to the process as the omission of some of the inputs can have a large effect on efficiency score. In this study, an example deals with the efficiency in the economic performance of 28 Chinese cities. Efficiency scores are calculated for all possible DEA model specifications. The results are analyzed using Principal Component Analysis and a new method for model selection is proposed in this paper.
Abstract. Weibull distribution plays an important role in failure distribution modeling in reliability studies and the estimation of its parameters is essential in the most real applications. Maximum likelihood (ML) estimation is a common method, which is usually used to elaborate on the parameter estimation. The maximizing likelihood function formed for the parameter estimation of a three-parameter (3-p) Weibull distribution is a quite difficult problem. Hence, the heuristic approaches must be used to discover good solutions. Particle swarm optimization (PSO) is a population based heuristic optimization technique developed from swarm intelligence. The performance of PSO greatly depends on its control parameters such as inertia weight and acceleration coefficients. Slightly different parameter settings may direct to very different performance. This paper gives a comprehensive investigation of different PSO variants (according to inertia weight procedures, acceleration coefficients, particle size, and search space) in the parameter estimation problem of 3-p Weibull distribution. Three explanatory numerical examples are given to show that PSO approach variants exhibit a rapid convergence to the maximum value of the likelihood function in less iteration, provides accurate estimates and PSO method is satisfactory for the parameter estimation of the 3-p Weibull distribution.