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 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.

Research on Human Resource Allocation Model Based on SOM Neural Network

2022 ◽  
pp. 513-525
Author(s):  
Jing Xu ◽  
Bo Wang ◽  
Gihong Min
Keyword(s):  
Neural Network ◽  
Resource Allocation ◽  
Human Resources ◽  
Human Resource ◽  
Human Resources Management ◽  
Configuration Model ◽  
Resource Configuration ◽  
Multiple Risks ◽  
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 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.

A comparative study of different prediction models in predicting human resource demands

2021 ◽  
pp. 1-11
Author(s):  
Bo Huang
Keyword(s):  
Neural Network ◽  
Human Resources ◽  
Comparative Study ◽  
Human Resource ◽  
Prediction Models ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Prediction Methods ◽  
Bpnn Model ◽  
Good Agreement

This study analyzed three prediction models: ID model, GM (1,1) model and back-propagation neural network (BPNN) model. Firstly, the principles of the three models were introduced, and the prediction methods of the three models were analyzed. Then, taking enterprise A as an example, the demand for human resources was predicted, and the prediction results of the three models were compared. The results showed that the maximum and minimum errors were 240 people and 12 people respectively in the prediction results of the ID3 model and 64 people and 37 people respectively in the prediction results of the GM (1, 1) model; the errors of the BPNN model were smaller than ten people, and the minimum value of the BPNN model was three people, which was in good agreement with the actual value. The prediction of the human resource demand of enterprise A in the future five years with the BPNN model suggested that the demand for employees would growing rapidly. The results show that the BPNN model has better reliability and can be popularized and applied in practice.

Augment Human Resource Proximity Plumbing Cybernation

2021 ◽  
pp. 131-140
Author(s):  
Anant Deogaonkar ◽  
Sampada Nanoty ◽  
Archana Shrivastava ◽  
Geetika Jain
Keyword(s):  
Artificial Intelligence ◽  
Human Resources ◽  
Human Resource ◽  
Work Culture ◽  
Technological Transformation ◽  
The People ◽  
Human Energy ◽  
Technological Interventions ◽  
Business Acumen ◽  
Employee Experience

The expeditious proliferation of artificial intelligence in the mainstream has rejigged the simplest processes of the various sectors in the most efficient way. With the advent of the era of cybernation, the work culture has been curbed with the timely developments and upgradation of the technology. Cybernation has propelled the growth of every respective sector of the vast corporate diaspora with time. The main aim of the cybernation being that of smoothening the complex, bulk tasks which exploit mass human energy, has seen much success in its purpose so far. But certain domains of the corporate diaspora still await the technological transformation of their respective processes. One such prominent domain and the real fuel of the corporate diaspora, the human resource has yet to expand its purview to imbibe and imbue cybernation in its certain processes. Human resource domain being the custodian of the corporate, wherein it is for the people and by the people though with the niche of Industry 4.0 beholds more space to expand the angle of understanding the term resource for the human, than human as an element of resource in itself. Multifarious human resource processes can be enhanced further with apt utility of digitization in order to optimize the user interface and user experience, boosting the overall employee experience amidst the corporate. Several certain customary functions of the human resources entail the adaptation of automation in more nuanced way to evolve parallel with the digitalization. Moreover, the millennial era further looks up to a transformed human resource with higher echelons of functions to be performed, digitally evolved jobs, an automated work environment, work culture well acquainted with the artificial intelligence. The effect of cybernation on the business acumen of futuristic human resource leaders, working in the rapid concurrent era of disruptions, without losing the human touch, will carve the future human resource structure. Therefore, the intent of this chapter is to study the detailed implications of automation, digitalization, and cybernation in the domain of human resources and to study and examine the dynamically changing HR functions with technological interventions and disruptions by proposing a literature review.

Artificial Intelligence in Human Resource Practices With Challenges and Future Directions

2022 ◽  
pp. 222-230
Author(s):  
Himani Saini ◽  
Preeti Tarkar
Keyword(s):  
Artificial Intelligence ◽  
Resource Management ◽  
Human Resources ◽  
Human Resource Management ◽  
Human Resource ◽  
Science And Technology ◽  
Future Directions ◽  
Smart System ◽  
Advanced Technologies ◽  
The Future

Artificial intelligence is a branch of science and technology that has been used effectively over the decades in various fields, and now it has become an indispensable part of organizational practices as it is one of the leading technologies in the current era, and now there is an emerging trend of applying AI technologies within the businesses. The central necessity of human resource management is also majorly based on technological approaches as it became a potential need for any human resources department to perform its role in the development of the whole organization. Technologies based on AI are and will be the smart system of the future and it's also changing the processes of human resource management by making it more dependent on advanced technologies. Through the chapter, the researcher will get to know the artificial technologies being practiced in HR practices and explore the probable and potential of technicality of AI in HRM and also the challenges associated with AI in HRM and its future possibilities.

Strategic Human Resource Allocation for an Internal Labor Market

2002 ◽  
pp. 299-310
Author(s):  
Wilfred S.J. Geerlings ◽  
Alexander Verbraeck ◽  
Jon van Beusekom ◽  
Ron P.T. de Groot ◽  
Gino Damen
Keyword(s):  
Resource Allocation ◽  
Labor Market ◽  
Human Resources ◽  
Human Resource ◽  
Manpower Planning ◽  
Strategic Decisions ◽  
Business Objectives ◽  
Forecasting Models ◽  
The Future ◽  
Insight Into

Every organization needs a staff appropriate for its tasks in order to accomplish its business objectives, both now and in the future. To gain insight into the quality and number of staff needed in the future, human resource forecasting models are being used. This chapter addresses the design of a simulation model for human resources forecasting, which is being developed for the Chief of Naval Personnel, Royal Netherlands Navy. The aim is to provide the Director of Naval Manpower Planning with tools that give insight into the effects of strategic decisions on personnel buildup, and the effects of changes in personnel on reaching the organization’s business objectives.

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