scholarly journals Multi-Objective Human Resource Allocation Approach for Sustainable Traffic Management

2020 ◽  
Vol 17 (7) ◽  
pp. 2470 ◽  
Author(s):  
Soumendra Nath Sanyal ◽  
Izabela Nielsen ◽  
Subrata Saha
Keyword(s):  
Resource Allocation ◽  
Human Resources ◽  
Human Resource ◽  
Traffic Management ◽  
Optimal Allocation ◽  
Road Traffic ◽  
Programming Model ◽  
Binary Programming ◽  
Multi Objective ◽  
Road Segments

Efficient human resource deployment is one of the key aspects of road traffic management for maintaining the lifelines of any metropolitan city. The problem becomes relevant when collaboration between human resources with different skills in day-to-day operations is necessary to maintain public and commercial transport, manage various social events and emergency situations, and hence reduce congestion, injuries, emissions, etc. This study proposes a two-phase fuzzy multi-objective binary programming model for optimal allocation of five different categories of human resources to minimize the overall operational cost, maximize the allocation to accident-prone road segments, minimize the number of volunteer personnel and maximize the direct contact to reduce emissions and road traffic violations, simultaneously. A binary programming model is formulated to provide an efficient individual manpower allocation schedule for multiple road segments at different shifts. A case study is proposed for model evaluation and to derive managerial implications. The proposed model can be used to draw insights into human resource allocation planning in traffic management to reduce road traffic congestion, injuries and vehicular emissions.

A Multi-Objective Programming Model of Dynamic Adjusting Flight Traffic

2014 ◽  
Vol 538 ◽  
pp. 127-133 ◽  
Author(s):  
Zhao Ning Zhang ◽  
Zhong Zhou Hao ◽  
Zheng Gao
Keyword(s):  
Load Balance ◽  
Optimal Allocation ◽  
Least Squares Method ◽  
Programming Model ◽  
Limiting Factors ◽  
Flight Delays ◽  
Traffic Demand ◽  
Multi Objective ◽  
Multi Objective Programming ◽  
Objective Programming

To alleviate the conflicts between the current flight traffic demand and the resource constraints of airspace, we need to improve the restrictions of flow allocation caused by the static air traffic flow allocation mode. The author analyzes the optimal allocation problem of dynamic adjusting flight flow and draws the conclusion that the problem should satisfy multiple targets, such as low flight delays, low flight cost and balancing the load of the route. Then consider a variety of limiting factors, such as the capacity of the route, flight planning, emergency situations, etc. Then establish multi-objective programming model of dynamic adjusting flight traffic. The objective function is determined by the flight cost, the flight delays and the value of the load balance. And the value of the load balance was first proposed according to the idea of least squares method. Then solve the model based on linear weighted technique. Finally the numerical result shows that the model can satisfy the multiple objectives and dynamic adjust the flight traffic optimally, that proves the rationality and validity of the model and the algorithm.

scholarly journals Optimization of Enterprise Labor Resource Allocation Based on Quality Optimization Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Gaoyang Liang ◽  
Long Xu ◽  
Liang Chen
Keyword(s):  
Resource Allocation ◽  
Human Resource ◽  
Optimal Allocation ◽  
Vital Role ◽  
Corporate Image ◽  
Skill Level ◽  
Systematic Research ◽  
Quality Optimization ◽  
Project Quality ◽  
Labor Resources

Companies take project-based management as their organizational strategy, and project quality assurance plays a vital role in improving customer satisfaction and enhancing corporate image. Starting from the perspective of optimizing project quality, this paper assigns different quality influencing factors to each project and each task of the project, divides the labor resources shared by multiple projects in the enterprise according to the skill level, and transforms the problem of project quality optimization. The problem of the highest skill level of labor resources allocated to all projects of the enterprise is designed, and algorithms are designed to achieve the optimization of project quality through the optimal allocation of labor resources. The various links in this article are closely related to form a comprehensive, scientific, and systematic research system for optimal human resource allocation, human resource management, and development. Finally, case analysis is used to confirm the usability of the model and provide a quantitative method and perspective for project-oriented companies to allocate workforce.

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.

Multi-Objective Optimization of Human Resource Allocation in Production Cells with Different Training Modes

2012 ◽  
Vol 601 ◽  
pp. 521-525
Author(s):  
Cai Juan Li ◽  
Xiao Yun Wu ◽  
Xiao Dong Zhang
Keyword(s):  
Resource Allocation ◽  
Staff Training ◽  
Programming Model ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
The People ◽  
Training Mode ◽  
The Difference ◽  
Production Cell

Aiming at the difference of the people as a particularity resource。In this paper ,the personnel training mode is divided into junior and senior, and a multi-objective integer programming model is established at the lowest cost of staff training, the highest man-machine adaptability degree and minimum personnel workload. Calculating example of a real production cell is presented. The results show that the model is correct and the necessity for classification of training modes.The model can help the management to adopt reasonable training mode and achieve desirable objectives.

scholarly journals Impact of Social Welfare Metrics on Energy Allocation in Multi-Objective Optimization

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2961
Author(s):  
Anders Clausen ◽  
Aisha Umair ◽  
Yves Demazeau ◽  
Bo Nørregaard Jørgensen
Keyword(s):  
Resource Allocation ◽  
Social Welfare ◽  
Energy Demand ◽  
Optimal Allocation ◽  
Supply And Demand ◽  
Multi Objective Optimization ◽  
Multi Agent Systems ◽  
Multi Objective ◽  
Pareto Sets ◽  
The Impact

Resource allocation problems are at the core of the smart grid where energy supply and demand must match. Multi-objective optimization can be applied in such cases to find the optimal allocation of energy resources among consumers considering energy domain factors such as variable and intermittent production, market prices, or demand response events. In this regard, this paper considers consumer energy demand and system-wide energy constraints to be individual objectives and optimization variables to be the allocation of energy over time to each of the consumers. This paper considers a case in which multi-objective optimization is used to generate Pareto sets of solutions containing possible allocations for multiple energy intensive consumers constituted by commercial greenhouse growers. We consider the problem of selecting a final solution from these Pareto sets, one of maximizing the social welfare between objectives. Social welfare is a set of metrics often applied to multi-agent systems to evaluate the overall system performance. We introduce and apply social welfare ordering using different social welfare metrics to select solutions from these sets to investigate the impact of the type of social welfare metric on the optimization outcome. The results of our experiments indicate how different social welfare metrics affect the optimization outcome and how that translates to general resource allocation strategies.

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 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.

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