Search

As an important economic sector, logistics is becoming more important, if not crucial, in economic growth. In our nation, the logistics industry is booming, and it's just getting better. However, in addition to focusing on the positive aspects of our country's logistics industry's development, we should also analyze and address the negative aspects of our country's logistics industry's development. The overall logistics pattern has not yet been formed, and there is an urgent need for systematic construction. The regional development is extremely unbalanced. By comparing the logistics performance indices of various Belt and Road countries, this research aims to examine the major elements influencing overall logistics performance. Second, we introduce the Moran index to explore the geographical association of the subdivision indicators of the logistics performance index using the spatial econometric model. The bootstrap DEA analysis method examines and ranks the countries' logistics performance indexes, determines our country's advantages and disadvantages in comparison to other Belt and Road countries, and executes specific improvement strategies that will enhance logistics and boost the overall growth of our country's logistics sector. [ABSTRACT FROM AUTHOR]

In the digital era, data mining and statistical analysis have been widely used to solve problems, especially in the field of management and engineering. Therefore, we aim to make a new insight of human resource management based on multiple regression modelling and quantile regression modelling. Specifically, the systematic framework of job satisfaction in this research is constructed by three dimensions from the perspective of psychology, namely, the perception of interpersonal relationship, financial compensation, and work conditions. Each dimension consists of two measures which reflect the employees' view towards them. The empirical estimation results show the following. (1) Perceived relationship with managers, perceived rationality of compensation, perceived match degree of job, and perceived autonomy degree of work are all significantly positively correlated with job satisfaction. (2) The effect of perceived rationality of compensation is significantly different between the high quantile and the low quantile. For those with lower perceived rationality of compensation, their job satisfaction is more likely to be affected due to the perceived compensation than those with higher perception. This research enriches the existing theory by constructing a comprehensive framework of the influencing factors of job satisfaction, which provides useful implications of human resource management optimization for enterprises. [ABSTRACT FROM AUTHOR]

The sudden outbreak of COVID-19 has a great impact on human life security and global economic development. To deal with the rampant pandemic, many countries have taken strict control measures, including restricting gathering in public places and stopping the production of enterprises; as a result, many enterprises suffered great challenges in survival and development during the pandemic. In order to help enterprises monitor their own financial situation and realize their healthy development under the pandemic, this paper constructs an Enterprise Financial Early Warning Model, in which Quantum Rotation Gate is used to optimize four algorithms, namely, Fruit Fly Optimization Algorithm (QFOA), Bee Colony Optimization Algorithm (QABC), Particle Swarm Optimization (QPSO), and Ant Colony Optimization (QACO). The results show that the ability of the prediction model can be greatly improved by using the Quantum Rotation Gate to optimize these four algorithms. [ABSTRACT FROM AUTHOR]

Unlike the previous study, an optimized neutral model, a new evolutionary calculation method, is applied in this study to predict the net value of domestic mutual funds. Firstly, 17 open-end balanced stock funds data will be collected from domestic securities companies' websites. Funds with the technical efficiency value of 1 will be selected as investment targets to analyze fund performance by using data envelopment analysis. Then, the mutual fund net worth prediction model is built by various new data mining methods including Backpropagation Neural Network and GABPN, and the forecasting ability is compared with the traditional regression model. We can understand the pros and cons of these fund forecasting models after using five kinds of forecast performance evaluation indicators and analyzing rate of return, and the result of which is available for reference to researchers and investors as an investment strategy. [ABSTRACT FROM AUTHOR]

From the perspective of the cost analysis of commodity marketing, the logistics cost accounts for about 30% of the final price of commodities, and the cost of storage selection accounts for about 75% of all logistics costs. This means that the management of the operation efficiency of storage is an essential part of logistics management. With four different modified fruit fly optimization algorithms, this study aimed to optimize the logistics storage selection. First, this study selected 10, 20, and 30 freight sections in a random manner; second, four fruit fly optimization algorithms were used to create some populations and fruit flies, which were taken as freight section spots; third, the algorithms were adopted to seek the logistics storage selection that costs the shortest time. According to analysis of the result, the chaos fruit fly algorithm, out of the four, was the one that took the shortest time and created the greatest effect when 10, 20, or 30 freight sections were considered. The 100 repetitious experiments also demonstrated that the chaos fruit fly algorithm cost the shortest time in the selection, and had the lowest time value variable. [ABSTRACT FROM AUTHOR]

Bank service satisfaction is vital to the success of a bank. In this paper, we propose to use the grey relational analysis to gauge the levels of service satisfaction of the banks. With the grey relational analysis, we compared the effects of different variables on service satisfaction. We gave ranks to the banks according to their levels of service satisfaction. We further used the quantile regression model to find the variables that affected the satisfaction of a customer at a specific quantile of satisfaction level. The result of the quantile regression analysis provided a bank manager with information to formulate policies to further promote satisfaction of the customers at different quantiles of satisfaction level. We also compared the prediction accuracies of the regression models at different quantiles. The experiment result showed that, among the seven quantile regression models, the median regression model has the best performance in terms of RMSE, RTIC, and CE performance measures. [ABSTRACT FROM AUTHOR]

This paper is totally different than the research design and research method in the related literature for investigating how information technology-based reading and learning process of network distance teaching affects the assessment result in the past, that is, innovative research architecture and process is adopted. Here, quantile regression analysis is applied to the investigation of how the time and frequency of log-in curriculum, browsing teaching material, and curriculum discussion in learning process record affects the final-term assessment result of multimedia design digital teaching material subject. In depth research is done under such research architecture, and it is hoped that how each independent variable affects the final-term assessment result under different quantile can be investigated. In addition, this paper has applied new artificial neural network technology to set up expert system for teacher's assessment result in distance teaching so as to reduce teacher's teaching pressure, moreover, the result can be used as reference by general researchers and scientific education researchers. The research result shows that the use of quantile regression to analyze the influence of different variable on the teacher's final-term assessment result of distance teaching is a feasible way; FOAGRNN model, as compared to other five models, has better forecasting capability. [ABSTRACT FROM AUTHOR]

Since Taiwan and mainland China signed the Economic Cooperation Framework Agreement (ECFA) across the Taiwan Strait, the number of mainland tourists visiting Taiwan has grown significantly. To cope with the needs of mainland tourists, Taiwan must reinforce the software and hardware facilities and service quality of its entire tourism industry. This will attract more tourists to Taiwan and create more opportunities for the Taiwanese tourism industry. In this article, tourists visiting Taiwan are asked to complete a questionnaire survey; we then use the satisfaction information gathered to perform grey relational analysis so as to understand the best and worst scoring questions related to satisfaction. From the analysis results, it can be seen that in the assessment of satisfaction question performance, Taiwanese cuisine scores the highest, the cleanliness of Taiwan's streets scores the lowest, and of people interviewed between 30 and 40 years old, more rated satisfaction performance and characteristics negatively. [ABSTRACT FROM AUTHOR]

Evolutionary computation is a computing mode established by practically simulating natural evolutionary processes based on the concept of Darwinian Theory, and it is a common research method. The main contribution of this paper was to reinforce the function of searching for the optimised solution using the fruit fly optimization algorithm (FOA), in order to avoid the acquisition of local extremum solutions. The evolutionary computation has grown to include the concepts of animal foraging behaviour and group behaviour. This study discussed three common evolutionary computation methods and compared them with the modified fruit fly optimization algorithm (MFOA). It further investigated the ability of the three mathematical functions in computing extreme values, as well as the algorithm execution speed and the forecast ability of the forecasting model built using the optimised general regression neural network (GRNN) parameters. The findings indicated that there was no obvious difference between particle swarm optimization and the MFOA in regards to the ability to compute extreme values; however, they were both better than the artificial fish swarm algorithm and FOA. In addition, the MFOA performed better than the particle swarm optimization in regards to the algorithm execution speed, and the forecast ability of the forecasting model built using the MFOA's GRNN parameters was better than that of the other three forecasting models. [ABSTRACT FROM PUBLISHER]

Abstract: The treatment of an optimization problem is a problem that is commonly researched and discussed by scholars from all kinds of fields. If the problem cannot be optimized in dealing with things, usually lots of human power and capital will be wasted, and in the worst case, it could lead to failure and wasted efforts. Therefore, in this article, a much simpler and more robust optimization algorithm compared with the complicated optimization method proposed by past scholars is proposed; the Fruit Fly Optimization Algorithm. In this article, throughout the process of finding the maximal value and minimal value of a function, the function of this algorithm is tested repeatedly, in the mean time, the population size and characteristic is also investigated. Moreover, the financial distress data of Taiwan’s enterprise is further collected, and the fruit fly algorithm optimized General Regression Neural Network, General Regression Neural Network and Multiple Regression are adopted to construct a financial distress model. It is found in this article that the RMSE value of the Fruit Fly Optimization Algorithm optimized General Regression Neural Network model has a very good convergence, and the model also has a very good classification and prediction capability. [Copyright &y& Elsevier]

Abstract: Hard clustering and fuzzy clustering analysis is the basis of the construction of many classifications and systems with its main focus on planning and dividing data into many subsets according to certain rules. Due to the practical function of clustering analysis, many researchers thus proposed different clustering algorithms to be used by researchers around the world. In this article, Fuzzy sammon Mapping method is implemented to perform clustering effect and classification capability analysis on these frequently used clustering algorithms. From the result of test data of an investigation of banking service satisfaction, GK Cluster algorithm was found to have very good clustering effect; however, as for classification capability, hard clustering analysis method has proved to be the better approach amongst the two. [Copyright &y& Elsevier]

Classification of operating performance of the enterprises is not only a hot issue emphasized by the management, but it is an important reference for investors too in their decision-making. Generally speaking, when predicting or analyzing business performance classification, most researchers adopt corporate financial early warning or credit-rating models, which pretty much use previous data and facts. Therefore, this paper brings about an alternative method to discriminate between excellent and poor business management, so as to take preventive measures prior to business crisis or bankruptcy. We collected the financial reports and financial ratios from the listed firms in mainland China and Taiwan as our samples to build up four kinds of forecasting models for business performance. The empirical results show that the hybrid model provides better classification forecasting capability than the other models, while the ANFIS model adjusted by genetic algorithm could effectively enhance the classification forecasting capability. [ABSTRACT FROM AUTHOR]

A clustering algorithm is a solution for grouping a set of objects and for distribution centre location problems. But the common K-means clustering algorithm may give local optimal solutions. Swarm intelligent algorithms simulate the social behaviours of animals and avoid local optimal solutions. We employ three swarm intelligent algorithms to avoid these solutions. We propose a new algorithm for the clustering problem, the fruit-fly optimization K-means algorithm (FOA K-means). We designed a distribution centre location problem and three clustering indicators to evaluate the performance of algorithms. We compare the algorithms of K-means with the ant colony optimization algorithm (ACO K-means), particle swarm optimization algorithm (PSO K-means), and fruit-fly optimization algorithm. We find K-Means modified by the fruit-fly optimization algorithm (FOA K-means) has the best performance on convergence speed and three clustering indicators, compactness, separation, and integration. Thus, we can apply FOA K-means to improve the distribution centre location solution and the efficiency for distribution in the future. [ABSTRACT FROM AUTHOR]

Lean production and service means to improve the production and service management within an enterprise. Through several management techniques, the waste, the redundant things, and the expenses without added values can be cleared, and the production and service within an enterprise will become smoother, that is, the competitiveness of an enterprise will be enhanced. In this study, the data collected from experiment carried out in Toyota lean service simulation laboratory of China University of Technology are used for analysis. Data mining technique is used to investigate whether the result of lean production and service taken by enterprises can enhance the entire performance of production and service. In this study, Grey relational analysis is performed first and is used to judge whether the data collected in the experiment using lean production and service can enhance performance; then, clustering method is used to classify experimental data into two clusters based on service attitude and dish-serving efficiency; finally, three data mining techniques of Genetic Programming (GP), Back-propagation Artificial Neural Network and logistic regression are used to set up, respectively, lean service performance model and Employee Characteristic Analysis model. From the analysis result, it is shown that the result of lean production and service can indeed enhance the performance of entire production and service; and among the three data mining techniques, GP model has the best classification and forecast capability. [ABSTRACT FROM AUTHOR]

Taiwan stock market trend is fast changing. It is affected by not only the individual investors and the three major institutional investors, but also impacted by domestic political and economic situations. Therefore, to precisely grasp the stock market movement, one must build a perfect stock forecast model. In this article, we used a multi-stage optimized stock forecast model to grasp the changing trend of the stock market. First, data of 2 stocks, TSMC and UMC were collected, and then inputted the test data into the genetic programing and built a model to find out the arithmetic expressions. Artificial Fish Swarm Algorithm is used to dynamically adjust the variable factors and constant factors in the arithmetic expressions. Next, we took the error term (ε) in arithmetic expressions to Gray Model Neural Network to make the forecast. Finally, we used the Artificial Fish Swarm Algorithm to dynamically adjust the parameters of the Gray Model Neural Network to enhance the precision of the stock forecast model as a whole. The result showed that the forecast capability of each stage after the optimization process is better than that of its previous stage, and the mixed stock forecast model (GP-AFSA+GMNN-AFSA) in stage 4 greatly enhanced the precision of the forecast. [ABSTRACT FROM AUTHOR]

The Fruit Fly Optimization Algorithm (FOA) is a widely used intelligent evolutionary algorithm with a simple structure that requires only simple parameters. However, its limited search space and the swarm diversity weaken its global search ability. To tackle this limitation, this paper proposes a novel Multi-Scale cooperative mutation Fruit Fly Optimization Algorithm (MSFOA). First, we analyze the convergence of FOA theoretically and demonstrate that its convergence depends on the initial location of the swarm. Second, a Multi-Scale Cooperative Mutation (MSCM) mechanism is introduced that tackles the limitation of local optimum. Finally, the effectiveness of MSFOA is evaluated based on 29 benchmark functions. The experimental results show that MSFOA significantly outperforms the improved versions of FOA presented in recent literature, including IFFO, CFOA, and CMFOA, on most benchmark functions. [ABSTRACT FROM AUTHOR]

At present, some classroom teaching evaluation methods are actually difficult to reasonably and fairly evaluate teachers’ true teaching level and classroom teaching quality. There are many shortcomings and drawbacks. In order to overcome the shortcomings of traditional teaching evaluation and enhance the evaluation effect, this paper combines the current advanced management methods with the merits of traditional evaluation methods, and applies the fuzzy evaluation method to combine qualitative assessment and quantitative assessment. First of all, establish a scientific and reasonable classroom teaching quality assessment index system, determine the standard assessment criteria, and then select and use the fuzzy comprehensive evaluation model to evaluate the classroom teaching quality, thus establishing a complete and effective teaching quality evaluation system. [ABSTRACT FROM AUTHOR]