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 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.

An Efficient Multi-Core Resource Allocation using the Multi-Level Objective Functions in Cloud Environment

2020 ◽  
Vol 13 (5) ◽  
pp. 957-964
Author(s):  
Siva Rama Krishna ◽  
Mohammed Ali Hussain
Keyword(s):  
Resource Allocation ◽  
Real Time ◽  
Cloud Services ◽  
Management Technique ◽  
Cloud Environment ◽  
Allocation Model ◽  
Proposed Model ◽  
Cloud Server ◽  
Different Types ◽  
Multi Level

Background: In recent years, the computational memory and energy conservation have become a major problem in cloud computing environment due to the increase in data size and computing resources. Since, most of the different cloud providers offer different cloud services and resources use limited number of user’s applications. Objective: The main objective of this work is to design and implement a cloud resource allocation and resources scheduling model in the cloud environment. Methods: In the proposed model, a novel cloud server to resource management technique is proposed on real-time cloud environment to minimize the cost and time. In this model different types of cloud resources and its services are scheduled using multi-level objective constraint programming. Proposed cloud server-based resource allocation model is based on optimization functions to minimize the resource allocation time and cost. Results: Experimental results proved that the proposed model has high computational resource allocation time and cost compared to the existing resource allocation models. Conclusion: This cloud service and resource optimization model is efficiently implemented and tested in real-time cloud instances with different types of services and resource sets.

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 Optimal Allocation of Human Resource Structure Based on Capability Maturity Model Integration

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Li Hao
Keyword(s):  
Resource Allocation ◽  
Human Resource ◽  
Optimal Allocation ◽  
Economic Benefits ◽  
Capability Maturity Model ◽  
Maturity Model ◽  
Allocation Model ◽  
Capability Maturity ◽  
Resource Allocation Model ◽  
Resource Structure

In order to improve the economic benefits of enterprises and provide a scientific human resource management method for enterprises, an optimal allocation method of human resource structure based on the integration of capability maturity model is proposed. According to the capability maturity model and its maturity level, the capability maturity integration model is established, and the optimal allocation algorithm of human resources is designed according to the model principle. By constructing the personnel quality evaluation matrix and personnel allocation matrix, the human resource allocation model is established, and the cooperative game method is used to dynamically optimize the human resource allocation model. The experimental results show that this method effectively improves the economic benefits of enterprises, improves the efficiency of human resource allocation, and completes the preset goal.

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.

Dynamic Game Algorithm for Allocation Model of Human Resource Based on Multi-Projects

2014 ◽  
Vol 1006-1007 ◽  
pp. 447-452
Author(s):  
Xu Ying Zhu ◽  
Kai Hu Hou ◽  
Cheng Chen ◽  
Fei Zhang ◽  
Li Yin Cao
Keyword(s):  
Game Theory ◽  
Resource Allocation ◽  
Human Resource ◽  
State Transition ◽  
Dynamic Game ◽  
Allocation Model ◽  
Time Dynamic ◽  
State Variable ◽  
Optimal Function ◽  
Stage 1

This paper focuses on the human resource allocation problems in multi-projects.The model of multi-project human resource allocation was established.Dynamic programming and matching game theory was adapted,a real-time dynamic game algorithm was proposed.the algorithm has adapted multidimensional state variable input method.In the stage 0, input A1, A2, A3,…,An and get the optimal variables’et by the filtration of the optimal function set.These variables enter the stage 1 by the help of the state transition function.Under the limit of B, these variables are filtrated by the optimal function to get the optimal strategies.As variables, these strategies enter the stage 2, and so on.Finally,the optimal scheme of the resource allocation between each other has been formed.Through the practical example test,the model and algorithm was found feasible and valid.

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