scholarly journals Personalized Resource Allocation in Wireless Networks: An AI-Enabled and Big Data-Driven Multi-Objective Optimization

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 144592-144609
Author(s):  
Rawan Alkurd ◽  
Ibrahim Y. Abualhaol ◽  
Halim Yanikomeroglu
Keyword(s):  
Resource Allocation ◽  
Wireless Networks ◽  
Big Data ◽  
Data Driven ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals Urban Crime and Security

2021 ◽  
pp. 213-228
Author(s):  
Tao Cheng ◽  
Tongxin Chen
Keyword(s):  
Resource Allocation ◽  
Big Data ◽  
Crime Prevention ◽  
Urban Crime ◽  
Data Driven ◽  
Big Data Technologies ◽  
Urban Security ◽  
Made In

AbstractScientists have an enduring interest in understanding urban crime and developing security strategies for mitigating this problem. This chapter reviews the progress made in this topic from historic criminology to data-driven policing. It first reviews the broad implications of urban security and its implementation in practice. Next, it focuses on the tools to prevent urban crime and improve security, from analytical crime hotspot mapping to police resource allocation. Finally, a manifesto of data-driven policing is proposed, with its practical demand for efficient security strategies and the development of big data technologies. It emphasizes that data-driven strategies could be applied in cities due to their promising effectiveness for crime prevention and security improvement.

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.

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