Optimization Algorithm of Preschool Education Resource Allocation Based on Data Envelopment Analysis Model

This study investigates the centralized resource allocation problem among individual units using network flow theory in a data envelopment analysis (DEA) framework. Existing research on DEA‐based resource allocation is primarily concerned with the amount of allocated resources to individual units, and only a few studies have considered resource flows when resources are allocated. To identify the internal structure of the resource allocation process, network structure is introduced and network flow theory is used to search for the optimal resource allocation scheme. Moreover, resource allocation cost is considered in network flows and a resource allocation gain (RAG) function is formulated to provide a quantitative analysis of the trade‐off between production gains from resource allocation and resource allocation cost in the resource allocation process. In addition, several characteristics of the RAG function are also discussed. Lastly, the proposed method is demonstrated through an expository example with several random data and an application of the centralized resource reallocation to bank systems.

In recent times, the advancement in network devices has focused entirely on the miniaturization of services that should ensure better connectivity between them via fifth generation (5G) technology. The 5G network communication aims to improve Quality of Service (QoS). However, the allocation of resources is a core problem that increases the complexity of packet scheduling. In this paper, a resource allocation model is developed using a novel deep learning algorithm for optimal resource allocation. The novel deep learning is formulated using the constraints associated with optimal radio resource allocation. The objective function design aims at reducing the system delay. The study predicts the traffic in a complex environment and allocates resources accordingly. The simulation was conducted to test the scheduling efficacy and the results showed an improved rate of allocation than the other methods.

Teaching resources in higher education are an important part of the talent cultivation system of higher education institutions. Dynamic management and optimal allocation of teaching resources are the focus of the construction of each school, which reflects the teaching, research, and management level of the school to a certain extent. In this paper, after combining the current situation and management problems of teaching resources in six schools in City A and putting forward constructive opinions, we further use data envelopment analysis to study the efficiency of teaching resource allocation in School H in City A and put forward the method of resource optimization.The input redundancy rate of School X1 in School H during the three years of 2021-2023 is 30.25%, 44.15%, and 55.26%, respectively. The data shows that the school has an excess of teacher inputs and there is a shortage of resources for teachers. For the current dynamic management and resource allocation problems of teaching resources in A city school, this paper argues that the management of school teaching resources is a long-term and complex organic whole, and needs to be reformed in terms of structure, resource integration and management, and to construct a high-quality and efficient management model. In terms of resource optimization and allocation, schools can optimize the talent resource allocation mechanism and other aspects to promote the full allocation of teaching resources in higher education.

The appreciative democratic voice of DEA: A case of faculty academic performance evaluation

Ship operations in the Arctic region require shipowners to increase specific safety investments due to the area's unique characteristics, such as extreme climate, remoteness, and fragile environment. From an economic perspective, shipowners must provide enhanced safety capacities through higher safety budgets while maintaining the profitability of ship operations. Therefore, economic assessments are necessary for decision-makers to determine whether safety investments are effectively allocated and to verify their adequacy. In this context, assessing safety input-output efficiency can help managers measure the efficiency of safety investments in terms of economic benefits and optimize resource allocation. This study uses Data Envelopment Analysis (DEA) to measure efficiencies over a selected period, using safety investments as inputs and economic benefits as outputs in the DEA models. The results of the empirical analysis of an Arctic ship operator indicate that safety investments were not effectively utilized in some years and show projected output targets for improvement. Additionally, recommendations for safety improvements are provided based on the DEA results and accident data. The approach in this study offers a practical tool for decision-makers to invest in Arctic shipping safety.

Optimal container resource allocation in cloud architecture: A new hybrid model

Relevance. Public health effectiveness is crucial for population health, especially in the face of global challenges like the COVID-19 pandemic. The study applies the Data Envelopment Analysis (DEA) model to measure the efficiency of operating costs in the health care system across various regions of Kazakhstan from 2017 to 2021. Existing methods for assessing healthcare effectiveness often overlook the system's complexity, which turns DEA into a valuable tool to identify inequalities in healthcare availability and quality. Research objective. This study aims to employ the DEA model to measure the efficiency of operating costs in the health care system across regions of Kazakhstan in 2017-2021. Data and methods. The DEA model was chosen for its ability to analyze the efficiency of operating costs. Data were collected from the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan. Results. Our findings indicate the need for increased healthcare financing in specific regions, emphasizing the importance of transparent spending. The study concludes that the DEA model can regularly assess health financing, ensuring resources are directed where most needed. The novelty lies in establishing a link between financing and health outcomes. Conclusions. The study's results and methodology can be used by public health authorities in assessing operating costs' effectiveness, allocating resources judiciously, and making informed decisions to enhance the healthcare system.

Data Envelopment Analysis (DEA) models and Free Disposal Hull (FDH) models were proposed almost simultaneously in the scientific literature. In DEA models, the constraints generate a convex set. For this reason, optimization methods and software are widely used for modeling and computations for these models. Non-convexity of production possibility set of FDH models refrained significantly the development of these models. In this paper, two methods are proposed for two-and three-dimensional frontier visualization for DEA and FDH models. Computational experiments using real-life datasets documented reliability and effectiveness of proposed methods.

This study aims to explore whether a machine learning algorithm can be used to make improvements in assessing unit efficiencies via a data envelopment analysis (DEA) model. In this study, a DEA model is used to calculate the efficiency scores of Desicion Making Units (DMUs). Then, an ML algorithm is trained that aims to predict the single output using inputs. Ranking of input features based on relative feature importance values obtained from the trained ML model is fed to the DEA model as weight restrictions. As a result, the two DEA models are compared with each other. ML-based insights (feature importance ranking) improve the DEA model in the direction of fewer zero weights. The additional weight restrictions are data depdendent, and hence realistic. As a novel approach, this study proposes the use of machine learning-based feature importance values to overcome a limitation of a DEA model.

Data envelopment analysis (DEA) is one of often used modeling tools for efficiency and performance evaluation of decision making units. Ratio DEA (DEA-R) is a group of novel mathematical models that combines standard DEA methodology and ratio analysis. The efficiency score given by standard DEA CCR model is less than or equal to that given by DEA-R model. In case of single input or single output the efficiency scores in CCR and DEA-R models are identical. The paper deals with DEA-R models without explicit inputs, i.e. models where only pure outputs or index data are taken into account. A basic DEA-R model without explicit inputs is formulated and a relation between output-oriented DEA models without explicit inputs and output-oriented DEA-R models is analyzed. Central resource allocation and slack-based measure models within DEA-R framework are examined. Finally they are used for projections of decision making units on the efficient frontier. The results of the proposed models are applied for efficiency evaluation of 15 units (Chinese research institutes) and they are discussed.

Construction and application of a refined model for the optimal allocation of water resources — Taking Guantao County, China as an example

One of the major concerns in healthcare management is the optimal allocation of staffing and resources. The global crisis created by the COVID-19 Pandemic has created extreme strains both on the staffing and the resources available to the healthcare systems. It has geometrically added to the number of patients seeking health services. In addition, the Pandemic has dramatically increased the rate of mortality in the hospitals in an unprecedented way. Therefore, in this research, in order not to sacrifice the quality of the services provided, we have used Data Envelopment Analysis (DEA) model to determine the efficiency of the emergency departments in the hospitals and the possible improvements that could be made to them. As traditional DEA models do not seek to reduce the undesirable outputs and increase the undesirable inputs, in addition to determining the efficiency of decision making units (DMU) despite some undesirable input components, the effect of these units on performance is investigated. To this end, considering the problem assumptions, first, a set of proper production possibilities is defined. Finally, a new method is introduced to determine the system’s performance in the presence of some undesirable input components. The impact of undesirable components on determining efficiency is specified, and a real example is provided consisting of emergency rooms in 30 hospitals, in which five desirable inputs and four desirable outputs along with one undesirable input are considered. The example is solved using the presented model, and the efficiency scores are determined.

A generalized model for data envelopment analysis with interval data

Data envelopment analysis (DEA) is a non-parametric technique for measuring the efficiency of decision making units (DMU) with exact input and output terms. So far, a number of DEA models with interval data have been developed. The radial DEA model such as CCR or BCC model with interval data are well known as basic DEA models with interval data. However, these radial DEA models have some defects. In this study, we develop a new approach based upon the slacks-based measurement (SBM) of super-efficiency model for dealing with interval data in DEA. The SBM super-efficiency DEA model can not only discriminate the performance of efficient DMUs from inefficient ones, but can distinguish between the efficient DMUs. In addition, this model can overcome the drawback that the super-efficiency model can be infeasible under variable returns to scale (VRS) assumption. An illustrative example is provided to verify the idea of this paper and show its potential application and validity.

Research and development (R&D) metrics influence research decisions, research efforts, resources allocation, performance control, and eventually innovation. Increasing concerns have emerged regarding how characteristic differences can significantly affect output as well as regarding the relative performance and productivity associated within the high tech industry. The primary objective of this study is to apply a new variant of the data envelopment analysis (DEA) model to examine the influence of specific organizational factors on high tech firm performance through system-DEA (SDEA) models, summarizing the insights in the process. Such insights are obtained using a new procedure for measuring high tech firm efficiency in R&D innovation productivity that takes into account specific organizational factors and variety. The SDEA divides decision-making units into homogenous and non-homogenous efficiency frontier systems and then employs the rank-sum-statistics-test to identify different systems, comparing them to the basic BCC DEA model among various high tech firms. As such, this study advances the understanding of specific organizational factors by exploring a largely ignored aspect in extant literature-namely, the performance measurement of R&D innovation. The empirical results strongly demonstrate that applying the SDEA to the high tech industry can find that firms operating within specific organizational factors are less efficient than those within non-specific organizational factors. The key findings verify the mutual learning effects that occur in R&D innovation processes of homogenous and non-homogenous high-tech firms applying the different systems.