scholarly journals Design and Simulation of Human Resource Allocation Model Based on Double-Cycle Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qi Feng ◽  
Zixuan Feng ◽  
Xingren Su
Keyword(s):  
Neural Network ◽  
Resource Allocation ◽  
Human Resource ◽  
Computational Models ◽  
Utilization Rate ◽  
Job Matching ◽  
Allocation Model ◽  
Resource Allocation Algorithm ◽  
Training Quality ◽  
Hidden Layer

The rationalization of human resource management is helpful for enterprises to efficiently train talents in the field, improve the management mode, and increase the overall resource utilization rate of enterprises. The current computational models applied in the field of human resources are usually based on statistical computation, which can no longer meet the processing needs of massive data and do not take into account the hidden characteristics of data, which can easily lead to the problem of information scarcity. The paper combines recurrent convolutional neural network and traditional human resource allocation algorithm and designs a double recurrent neural network job matching recommendation algorithm applicable to the human resource field, which can improve the traditional algorithm data training quality problem. In the experimental part of the algorithm, the arithmetic F1 value in the paper is 0.823, which is 20.1% and 7.4% higher than the other two algorithms, respectively, indicating that the algorithm can improve the hidden layer features of the data and then improve the training quality of the data and improve the job matching and recommendation accuracy.

scholarly journals Human Resource Petri Net Allocation Model Based on Artificial Intelligence and Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Weihuang Dai ◽  
Yi Hu ◽  
Zijiang Zhu ◽  
Xiaofang Liao
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Resource Allocation ◽  
Human Resources ◽  
Human Resource ◽  
Petri Net ◽  
Resource Scheduling ◽  
Accuracy Rate ◽  
Allocation Model ◽  
Resource Data

The reasonable allocation and use of human resources is an important content in the process of complex system analysis and design. This paper studies the human resource allocation model of Petri net based on artificial intelligence and neural network. In this paper, combined with the characteristics of human resource scheduling, human resource mobility, concurrency, and obvious classification characteristics, the human resource allocation model based on Petri net is implemented. In this paper, the model is trained with the python version of human resource analysis data set. The training parameters are 100, the error coefficient is 0.001, and the learning speed is 0.01. First, the coding rules of human resource data are established. Then, the parameters are input into the model, and the human resource data are trained in the model. Finally, the results of the model output layer are analyzed. The research study shows that the average prediction accuracy of this model is 78.85%. Model training requires the addition of 25 neurons for every 0.01 increase to improve the accuracy of predicting dynamic data of human resources. If the accuracy rate exceeds 75%, the increase in the number of neurons cannot be compensated for by the increase in the accuracy rate, but it is most efficient when the amount of data for human resource scheduling is 2000 to 4000. Therefore, this system can effectively allocate small- and medium-sized human resources and has a high accuracy.

Research on Human Resource Allocation Model Based on SOM Neural Network

2019 ◽  
Vol 10 (1) ◽  
pp. 65-76
Author(s):  
Jing Xu ◽  
Bo Wang ◽  
Gihong Min
Keyword(s):  
Neural Network ◽  
Resource Allocation ◽  
Human Resources ◽  
Human Resource ◽  
Human Resources Management ◽  
Configuration Model ◽  
Allocation Model ◽  
Resource Configuration ◽  
Som Neural Network ◽  
Fierce Competition

With the fierce competition of the enterprise market, the human resource allocation of enterprises will face multiple risks. This article takes the connotation of human resource configuration management as the research object and establishes the human resource configuration model through SOM neural network. And the model is trained, learned, and tested. What's more, it is applied to human resources management to adjust the allocation of human resources for the enterprise in a timely manner. It provides a detailed basis for proposing coping strategies and has a great application value.

scholarly journals The research on collaborative optimization of emergency human resource allocation

2018 ◽  
Vol 17 ◽  
pp. 03016
Author(s):  
Qilin Li ◽  
Chuanliang Jia ◽  
Jiu Su
Keyword(s):  
Resource Allocation ◽  
Human Resource ◽  
Case Analysis ◽  
Collaborative Optimization ◽  
Government Officials ◽  
Allocation Model ◽  
Different Types ◽  
Potential Demand ◽  
Full Consideration ◽  
Tremendous Impact

On aglobal scale, the occurrence of different types of emergencies has had a tremendous impact on the economies and people's lives. The optimization of emergency human resource allocation is increasingly important. This paper gives full consideration to the control targets of each fire rescue points and the demands of both demand points and potential demand points. We build an emergency human resource allocation model and optimize it through the collaborative optimization. This paper finally carried on the case analysis to verify the feasibility of the model. The model better simulates the reality and can be referred by some government officials in some emergency cases.

scholarly journals Study of Economic Management Forecast and Optimized Resource Allocation Based on Cloud Computing and Neural Network

2020 ◽  
Author(s):  
Pinzhen He
Keyword(s):  
Neural Network ◽  
Resource Allocation ◽  
Cloud Computing ◽  
Linear Mapping ◽  
Global Market ◽  
Operating Efficiency ◽  
Economic Forecast ◽  
Economic Management ◽  
Trend Prediction ◽  
Allocation Model

Abstract As various factors which affect the development of global market economy become increasingly uncertain, economy and commodity will become more and more fluctuating in economic operation. With its strong non-linear mapping capacity, artificial neural network has already been applied in many fields, time series analysis and trend prediction. Cloud computing can interact fast with service provider at the minimum management cost. This paper proposes an economic forecast and optimized resource allocation model based on cloud computing and BP neural network. Its main goal is to break down a complex prediction task into several sub-tasks, effectively reduce the workload of a single computer and enhance the operating efficiency. Simulation results show that the proposed method does not rely on gradient information, and has strong optimization calculation ability. At the same time, it can analyze and predict economic management, so as to provide strong decision support for decision makers.

scholarly journals Human Resources Balanced Allocation Method Based on Deep Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Weiwei Shi ◽  
Qiuzuo Li
Keyword(s):  
Neural Network ◽  
Resource Allocation ◽  
Deep Learning ◽  
Human Resources ◽  
Human Resource ◽  
Recurrent Neural Network ◽  
Enterprise Management ◽  
Balanced Development ◽  
Balanced Allocation ◽  
Matching Degree

At present, the economics and social developments show the characteristics of diversification, and the focus of social enterprise management is driven by the allocation of human resources. Human resource allocation is a way of appropriate allocation and reasonable placement of human resources. It means that, under the guidance of science, human resources can maintain the best combination with other resources at any time. Nevertheless, the irregularities in management teams and the balanced differences of talent quality have a great effect on the balanced development of an enterprise. Based on this, this paper studies the establishment of a recurrent neural network (RNN) model to realize the allocation of human resources and the balanced development of enterprise management. Firstly, a deep learning model, based on the recurrent neural network, is established. Then, the human resources data is analyzed to calculate the matching degree between the human resources and posts. Finally, personnel scheduling is carried out according to the matching degree score between the human resources and posts, to obtain the optimal balanced allocation result of the human resources. Experimental results show that our method can bring significant improvements to personnel position matching and effectively enhance the efficiency of human resource allocation based on the cloud environment.

scholarly journals Resource Allocation for Edge Computing without Using Cloud Center in Smart Home Environment: A Pricing Approach

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6545
Author(s):  
Huan Liu ◽  
Shiyong Li ◽  
Wei Sun
Keyword(s):  
Resource Allocation ◽  
Home Environment ◽  
Smart Home ◽  
Smart Homes ◽  
Research Area ◽  
Edge Computing ◽  
Allocation Model ◽  
Resource Allocation Algorithm ◽  
Optimal Resource ◽  
Smart Home Environment

Recently, more and more smart homes have become one of important parts of home infrastructure. However, most of the smart home applications are not interconnected and remain isolated. They use the cloud center as the control platform, which increases the risk of link congestion and data security. Thus, in the future, smart homes based on edge computing without using cloud center become an important research area. In this paper, we assume that all applications in a smart home environment are composed of edge nodes and users. In order to maximize the utility of users, we assume that all users and edge nodes are placed in a market and formulate a pricing resource allocation model with utility maximization. We apply the Lagrangian method to analyze the model, so an edge node (provider in the market) allocates its resources to a user (customer in the market) based on the prices of resources and the utility related to the preference of users. To obtain the optimal resource allocation, we propose a pricing-based resource allocation algorithm by using low-pass filtering scheme and conform that the proposed algorithm can achieve an optimum within reasonable convergence times through some numerical examples.

scholarly journals Analysis of Human Resource Allocation Model for Tourism Industry Based on Improved BP Neural Network

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Shuo Zhu ◽  
Yan Liu
Keyword(s):  
Neural Network ◽  
Resource Allocation ◽  
Human Resource ◽  
Bp Neural Network ◽  
Short Term Memory ◽  
Random Perturbation ◽  
Tourism Industry ◽  
Short Term ◽  
Term Memory ◽  
Neural Network Algorithm

This paper analyzes the deficiencies of human resource allocation in the tourism industry by investigating the human resource allocation in the tourism industry, puts forward corresponding improvement measures and suggestions, and strives to provide certain guidance and helpful effects for the construction of tourism resource informatization. In this paper, a modified BP neural network model is proposed by introducing random perturbation terms on the hidden layer in the BP neural network algorithm, and the weight matrix connecting the input values is added with the random perturbation matrix to obtain a new weight matrix so that the convergence effect of the improved BP neural network algorithm is improved. Then, to address the problem that the initial weights of the long and short-term memory neural network and gated BP unit neural network have a large impact on the convergence speed and prediction accuracy of the algorithm after the initial weight selection is determined, this paper introduces the random perturbation term into the gate structure of the long and short-term memory neural network and gated BP unit neural network and proposes and connects an improved long and short-term memory neural network and gated BP unit neural network. The weight matrix of the input values is added with the random perturbation matrix to obtain the new weight matrix so that the convergence effect of the improved long and short-term memory neural network algorithm and the gated BP unit neural network algorithm is improved. Constructing the human resource allocation model of the tourism industry and proposing coping strategies and countermeasures and taking the human resource allocation system of the tourism industry as the core, the human resource allocation model of the tourism industry is established by combining the network image crisis life cycle system of tourism scenic spots and the network public opinion dissemination model. From the perspective of managers, the human resource allocation management policy and management procedures of the tourism industry are proposed. Using the quantifiable and disenable characteristics of online text information, the response strategy of online monitoring and propaganda and offline management and enhancement is proposed, and innovative countermeasures to the human resource allocation of the tourism industry are proposed in three categories: network originated, reality coexisting, and reality originated. Through this paper, we propose a new approach to human resource allocation management and development in the tourism industry and improve the efficiency of human resource allocation in the tourism industry.

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