Performance Evaluation Problem Research Articles

Comprehensive evaluation measures of nonlinear estimation algorithm performance

Although many scholars say that their algorithms are better than others in the state estimation problem, only a fewer convincing algorithms were applied to engineering practices. The reason is that their algorithms outperform others only in some aspects such as the estimation accuracy or the computation load. To solve the problem of performance evaluation of state estimation algorithms, in this paper, the comprehensive evaluation measures (CEM) for evaluating the nonlinear estimation algorithm (NEA) is proposed, which can comprehensively reflect the performance of the NEAs. First, we introduce three types of the NEAs. Second, the CEM combining the flatness, estimation accuracy and computation time of the NEAs, is designed to evaluate the above NEAs. Finally, the superiority of the CEM is verified by a numerical example, which helps decision makers of nonlinear estimation algorithms theoretically and technically.

A Performance Evaluation Method for Employees in Small and Medium-Sized IT Enterprises Based on Multiple Indicators

In recent years, research on performance evaluation of IT enterprises has become a hot topic worthy of attention. In some countries, IT enterprises have become essential in driving economic development. However, there are significant differences in personnel management, performance evaluation, and other aspects among listed IT enterprises, large IT enterprises, and small and medium-sized IT enterprises, making it difficult to standardize performance evaluation for different types of enterprises. Especially for small and medium-sized IT enterprises, there are still specific problems in performance evaluation due to the unique characteristics of the enterprise. The primary manifestation is that standardized evaluation indicators cannot be fully applicable to small and medium-sized IT enterprises, making it challenging to conduct dynamic evaluations based on the characteristics of the enterprise. This article is based on the research and analysis data of performance evaluation management for small and medium-sized IT enterprises in a particular region of China. It proposes a multi-indicator-oriented performance evaluation system for small and medium-sized IT enterprises and offers corresponding guarantee measures.

Study on the Application of Balanced Scorecard in A Company

Based on the theory of performance evaluation and the existing research, taking high-tech Electric Company A as the research object, the current situation and the existing problems in performance evaluation are analyzed. Thirdly, based on the balanced scorecard, the index system of performance evaluation is established from four dimensions. Finally, put forward targeted implementation suggestions. Through the research, the conclusion is drawn: the introduction of the balanced scorecard theory, the decomposition of enterprise strategic objectives, and the construction of the performance evaluation system index combining financial dimension and non-financial dimension can improve the performance and enterprise value of A enterprise.

Evaluation Mechanism for Decentralized Collaborative Pattern Learning in Heterogeneous Vehicular Networks

Collaborative machine learning, especially Federated Learning (FL), is widely used to build high-quality Machine Learning (ML) models in the Internet of Vehicles (IoV). In this paper, we study the performance evaluation problem in an inherently heterogeneous IoV, where the final models across the network are not identical and are computed on different standards. Previous studies assume that local agents are receiving data from the same phenomenon, and a same final model is fitted to them. However, this “one model fits all” approach leads to a biased performance evaluation of individual agents. We propose a general approach to measure the performance of individual agents, where the common knowledge and correlation between different agents are explored. Experimental results indicate that our evaluation scheme is efficient in these settings.

BORSA İSTANBUL’DA ENERJİ ŞİRKETLERİNİN NAKİT AKIM ORANLARINA DAYALI FİNANSAL PERFORMANS ANALİZİ

The purpose of this paper is to evaluate the financial performance of the sixteen energy companies listed in Borsa Istanbul in terms of cash sufficiency and cash efficiency and benchmark them in 2020 and 2021; during the COVID-19 period by using multi-criteria decision making methods. This paper utilizes the Method Based on the Removal Effects of Criteria (MEREC) technique to determine the objective weights of cash ratios. Energy companies’ ranking is estimated using the Grey Relational Analysis (GRA) approach. In the case of the financial performance evaluation problem, 7 performance attributes and 16 alternative layouts were considered. The results show that most of the energy companies have weak financial performance in 2020 and then improve their financial performance in 2021.

Capturing attitudinal characteristics of decision makers in multi-criterion sorting problems for performance evaluation of sustainable and circular suppliers

Capturing attitudinal characteristics of decision makers in multi-criterion sorting problems for performance evaluation of sustainable and circular suppliers

Performance evaluation of possibilistic fuzzy portfolios with different investor risk attitudes based on DEA approach

By considering the stock market’s fuzzy uncertainty and investors’ psychological factors, this paper studies the portfolio performance evaluation problems with different risk attitudes (optimistic, pessimistic, and neutral) by the Data Envelopment Analysis (DEA) approach. In this work, the return rates of assets are characterized as trapezoidal fuzzy numbers, whose membership functions with risk attitude parameters are described by exponential expression. Firstly, these characteristics with risk attitude are strictly derived including the possibilistic mean, variance, semi-variance, and semi-absolute deviation based on possibility theory. Secondly, three portfolio models (mean-variance, mean-semi-variance, and mean-semi-absolute-deviation) with different risk attitudes are proposed. Thirdly, we prove the real frontiers determined by our models are concave functions through mathematical theoretical derivation. In addition, two novel indicators are defined by difference and ratio formulas to characterize the correlation between DEA efficiency and portfolio efficiency. Finally, numerical examples are given to verify the feasibility and effectiveness of our model. No matter what risk attitude an investor holds, the DEA can generate approximate real frontiers. Correlation analysis indicates that our proposed approach outperforms in evaluating portfolios with risk attitudes. At the same time, our model is an improvement of Tsaur’s work (2013) which did not study the different risk measures, and an extension of Chen et al.’s work (2018) which only considered risk-neutral attitude.

Dynamic Process Modeling and Real-Time Performance Evaluation of Rework Production System With Small-Lot Order

Rework production system is a typical structure in practical manufacturing, where the defective parts detected by the inspection machine are sent to rework rather than being directly scrapped. Due to the special structure, more complex coupling phenomena between machines and buffers are introduced, especially when machines are unreliable and buffers are finite. In addition, under the intelligent manufacturing environment, customized demands trigger small-lot orders become a common occurrence, which lead to traditional steady-state analysis inapplicable. Therefore in this paper, we study dynamic process modeling and real-time performance evaluation problems of the rework system. Practical issues, such as unreliable machines, finite buffers, and small-lot orders are taken into account. Specifically, a novel dynamic decomposition and aggregation algorithm is proposed. The proposed method is validated to have a precise performance measures prediction effect, as well as an efficient computational performance.

Instance Space Analysis of Search-Based Software Testing

Search-based software testing (SBST) is now a mature area, with numerous techniques developed to tackle the challenging task of software testing. SBST techniques have shown promising results and have been successfully applied in the industry to automatically generate test cases for large and complex software systems. Their effectiveness, however, has been shown to be problem dependent. In this paper, we revisit the problem of objective performance evaluation of SBST techniques in light of recent methodological advances - in the form of Instance Space Analysis (ISA) - enabling the strengths and weaknesses of SBST techniques to be visualised and assessed across the broadest possible space of problem instances (software classes) from common benchmark datasets. We identify features of SBST problems that explain why a particular instance is hard for an SBST technique, reveal areas of hard and easy problems in the instance space of existing benchmark datasets, and identify the strengths and weaknesses of state-of-the-art SBST techniques. In addition, we examine the diversity and quality of common benchmark datasets used in experimental evaluations.

Research on the Performance Evaluation Method of Specialized English Course Based on CH-SPOC Mode

In order to solve the performance evaluation problem existed in the CH-SPOC mode specialized English course, process evaluation was combined with summative evaluation to design a specialized English course performance evaluation index system, in view of the characteristics of CH-SPOC mode and specialized English course teaching and the importance of different evaluation indexes. The evaluation index system was then applied to teaching practice. The research shows that this performance evaluation method not only reflects students' learning results, but also pays attention to students' learning process and the concept of collaborative learning in CH-SPOC mode, which can better stimulate learning enthusiasm and improve teaching effect.

Improved Whale Algorithm for Economic Load Dispatch Problem in Hydropower Plants and Comprehensive Performance Evaluation

A novel method for economic load dispatch problem (ELDP) based on improved whale optimization algorithm(IWOA) is presented, and the optimization performance of IWOA in ELDP was evaluated comprehensively. The search mechanism is modified to improve the ability of the algorithm to jump out of the local optimal. The adaptive nonlinear inertia weight is introduced to improve the convergence speed of the algorithm. A limited mutation mechanism is proposed to improve the convergence of the algorithm. The evaluation indicator of calculation time and calculation accuracy was established. Taking 26 units of the Three Gorges Hydropower Station as an example, limited adaptive genetic aigorithm (LAGA), particle swarm optimization (PSO), whale optimization algorithm (WOA) and improved whale optimization algorithm (IWOA) were used to solve ELDP. The result shows that IWOA is superior to other algorithms in calculation results of various heads and loads. The calculation accuracy of IWOA was better than WOA when the number of units turned on was more than 6. The analysis results of IWOA and DP show that the calculation time of IWOA is better than that of DP when the number of units turned on is more than 6. The IWOA and the evaluation indicators proposed in this paper provide a new way for solving ELDP of large hydropower stations.

Research on the International Trade Performance Evaluation of Cross-Border e-Commerce Based on the Deep Neural Network Model

The rapid development of e-commerce international trade has driven the rapid growth of the economic system of international trade enterprises. This also means that industry competition is gradually intensifying, which also makes performance evaluation the key to cross-border e-commerce international trade. At present, my country’s research on the performance evaluation of cross-border e-commerce international trade is in a blank state. Therefore, this paper takes the international trade performance evaluation of cross-border e-commerce as the research object and, based on the deep neural network model, develops a cross-border international trade performance evaluation model, changes trade strategies, and improves trade performance. This paper first analyzes various neural network models, such as artificial neural network, “BP” neuron model, and LSTM neural network. This paper summarizes a deep neural network model that is conducive to the development of cross-border e-commerce and points out the problems in the current performance evaluation of cross-border e-commerce international trade: the e-commerce market supervision system is not perfect; the second is the inconsistent evaluation indicators; the third is the evaluation system. There are some differences with the actual. Finally, this paper puts forward relevant suggestions for the performance evaluation of cross-border e-commerce international trade and points out the advantages and disadvantages of various neural networks, as well as their roles in cross-border e-commerce performance evaluation, and compares these neural networks through experiments. Experiments show that among these neural network models, the deep neural network model is the best and has the highest accuracy and stability in e-commerce trade performance evaluation. In the later stage, we will improve the global logistics system, strengthen the application of big data technology, and improve the overall performance of global operations. First, a set of indicators is designed to evaluate the performance of e-commerce systems, using the enterprise key factor model concept. In addition, this evaluation method is different from the commonly used expert evaluation method and physical evaluation method in evaluating the construction quality, cost, education and growth ability, and performance level of the international business system of cross-border e-commerce.

A Performance Evaluation and Analysis Method for Multi-Rotor UAV under Actuator LOE

Loss of Actuator Effectiveness (LOE) reduces the performance parameters of Multi-rotor Unmanned Aerial Vehicle (UAV), which affects the reliability of the mission. However, in practice, researchers used to evaluate the performance of a UAV through lots of flight experiments or by experience, which is normally inefficient and costly. In order to evaluate the performance of UAVs, this paper presents a performance evaluation and analysis method under actuator LOE. First, models are established for the components of a propulsion system, mainly including actuators and batteries. Second, the performance evaluation problem is simplified and decomposed into two sub-problems. performance indices are established by kinetic analysis and then solved, considering the influence of actuator LOE and battery performance. Finally, based on the analysis results, the evaluation method is demonstrated in several cases. The results showed that the evaluation method is effective compared with the simulation results of www.flyeval.com/. In addition, LOE reduces the performance parameters of the UAV, which has a greater impact on the maximum flight speed and maximum cruising distance.

Performance Evaluation Method of Public Administration Department Based on Improved DEA Algorithm.

This paper proposes a performance evaluation method of public administration departments based on the improved DEA algorithm, which solves the quality problem of performance evaluation of public administration departments and lays a foundation for the long-term development and performance improvement of public administration departments. The DEA model is the core to sort out the performance evaluation methods of public administration departments. From the perspective of DEA algorithm, whether DEA is effective is an important content that public administration departments must consider when carrying out performance administration, and public service satisfaction is the core parameter obtained by DEA algorithm. That is to say, by optimizing public services and improving service satisfaction, the public administration department can further improve the quality of performance evaluation. Therefore, we can carry out performance evaluation of public administration departments from the satisfaction of public services. The empirical research conclusion shows that according to the effective judgment theorem of DEA, it can be concluded that among the eight social security departments, there are five departments that can achieve DEA effectiveness, namely department 2, department 3, department 4, department 5, and department 6. There are three non-DEA valid ones, namely, department 1, department 7 and department 8. The public satisfaction and the total service cost will affect the performance quality to a certain extent, and only by balancing various influencing factors can the performance evaluation quality of the public administration department be maximized.

BP Neural Network-Based Evaluation Method for Enterprise Comprehensive Performance

Comprehensive performance evaluation is an important basis for improving the training effect of enterprise employees and the effective allocation of enterprise resources. Based on AHP and BP neural network theory, this paper constructs a comprehensive performance evaluation method for enterprises, AHP is used to calculate the weight of the index, and then the importance index is screened. The model proposes a conceptual model of comprehensive performance of manufacturing enterprises from the support layer, core layer, and promotion layer and constructs a manufacturing system from horizontal and vertical. The influencing factors of comprehensive performance solve the quantification problem of enterprise comprehensive performance evaluation and have obvious guiding value for the research on the integration mode and path of industrialization and industrialization of regional manufacturing enterprises. In the simulation process, the weight of each index in the evaluation system is first determined by the analytic hierarchy process; then the evaluation index membership score table is established, and fuzzy mathematics is used to calculate the expert’s score, so as to solve the problem caused by the intermediate value. The uncertainty caused by the jump is finally established by the analytic hierarchy process, and the neural network is used to simulate the sample. The experimental results show that by using AHP to collect training samples for neural network evaluation, the comprehensive performance evaluation system has good fitness and achieves the best comprehensive consideration of accuracy and training time when there are 17 hidden layer neurons. The maximum relative error is 1.64%, which is much lower than the general accuracy requirement of 5%, which effectively improves the performance and calculation accuracy of the network.

Study on Employee Performance Evaluation Based on Adaptive Feature Selection Fuzzy Algorithm

In our study, in terms of performance evaluation methods, the performance evaluation algorithm based on genetic algorithm and fuzzy comprehensive performance evaluation algorithm is introduced, their advantages and disadvantages are compared and analyzed, and the design idea of fuzzy performance evaluation algorithm based on compound elements is proposed. It can be divided into seven steps: first, clarify the evaluation purpose and object; second, select the optimal evaluation mode and method; third, compile the evaluation index system; fourth, it is to collect information extensively; fifth, the evaluation adopts a variety of methods, multiple angles, and multiple sides to collect materials so that the conclusion of the evaluation has sufficient factual basis; the sixth is to process the information and make a comprehensive evaluation; the seventh is to analyze the results and write an evaluation report. Based on the existing algorithms, the fuzzy performance evaluation algorithm based on compound elements is studied, and the detailed design of the algorithm is presented. Through the comparison and analysis of the performance evaluation algorithm based on compound elements and the performance evaluation algorithm of specific elements, the superiority of the performance evaluation algorithm based on compound elements is experimentally verified by comparing the operation time and classification accuracy. The performance evaluation system based on the performance evaluation algorithm designed in this study, combined with the official business of the performance evaluation system, modularizes the administrative management activities of the enterprise, digitizes the electronic office information, and conducts in-depth exploration of the unstructured natural language. A real-time performance evaluation system based on the arrangement of corporate administrative activities has been established. By designing and implementing a performance-assisted analysis system based on text content analysis, which is suitable for performance evaluation systems, it solves the imperfect problem of performance evaluation based on electronic enterprise administrative management.

Exploring the Application and Optimization Strategy of the LMBP Algorithm in Supply Chain Performance Evaluation.

In recent years, with the emergence of new technologies, big data, artificial intelligence, and other technologies have had a greater impact on supply chain management. Among them, big data analysis capability, as one of the important capabilities that supply chain enterprises should have, has a particularly significant impact on supply chain resilience management. From the perspective of performance management, based on supply chain resilience theory, the relationship between the supply chain performance management level, supply chain collaboration, and other supply chain resilience elements, as well as big data analysis capability and supply chain performance can be analyzed to study the impact of big data analysis capability on supply chain performance of enterprises of different scales. The impact on the level of supply chain performance is being studied. This paper investigates the problem of supply chain performance evaluation and optimization based on the LMBP algorithm and provides some references for supply chain performance evaluation and optimization.

Design of Performance Evaluation Algorithm for Diversified Talent Training in Modern Universities Considering Innovative Thinking

The global economic trends and the winds of technological change have elevated the status of integration between industry and education for innovation and entrepreneurship. Technologies and the Internet have significantly changed all aspects of our lives. College education has improved as a result of the enormous benefits of the Internet and big data. Reforms and development have still a lot of application space in the training and practice of educational technology talents of a college education. To improve the performance evaluation capabilities of educational technology experts in Chinese universities through education and “Internet +” has become a challenge that has to be addressed. This article is under such a formal background to have a detailed study about the current college education in China. In this study, an analysis is presented for the performance evaluation of the education technology talent training in modern universities based on big data technology. A fuzzy evaluation algorithm based on composite elements is proposed to evaluate the multielement and multilevel college education system. Experimental results illustrate the feasibility and effectiveness of the proposed algorithm. The proposed method has the potential to solve the complicated performance evaluation problem in colleges.

Integrated Evaluation of Corporate Investment Decision Performance Based on Fuzzy Sets and BP Neural Networks

Enterprise investment decision performance evaluation is a complex system, which is affected by many factors. Based on the consideration of corporate strategy and stakeholders, a six-level index system is designed to evaluate the performance of corporate investment decision-making. The index-selection method combining benchmarking management and principal component analysis is established, and the enterprise management index analysis method based on neural network and fuzzy decision-making is designed. The multiattribute decision-making problem in enterprise performance evaluation is solved by using the triangular fuzzy-weighted Einstein-Bonferroni mean (TF-WEBM). The algorithm is suitable for multiattribute decision-making in a triangular fuzzy environment. Finally, the effectiveness of this method is verified by an enterprise performance evaluation example.

Using The Analytic Hierarchy Process For Store Manager Selection: A Real Case Study

In today's competitive work environment, it is important for managers to have personnel who can move the company forward and adapt to changes as quickly as possible. Selecting the most suitable personnel has a great influence on the development of the company as well as on the motivation of the personnel. For this reason, the frequently encountered problem of personnel evaluation is a significant one. In this problem, the performance of the personnel is compared and evaluated considering the criteria set. Therefore, the personnel evaluation problem is a multi-criteria, decisionmaking problem. In this study, the personnel performance evaluation problem within Lanse Company was solved using the Analytic Hierarchy Process(AHP), a multi-criteria decision-making method. The criteria that are effective in personnel selection were determined through a literature review and interviews with the owner of the company and the Human Resources Team (HRT). Considering these criteria, a hierarchical model was constructed, and pairwise comparisons were made to obtain judgments. As a result of the pairwise comparisons, the most suitable person for the position of store manager was identified among the six employees working at Lanse. The results were presented to the decision maker in the form of a report. The validity and applicability of the findings were confirmed by the decision maker.