Solving Resource Management Optimization Problems in Contact Centers with Artificial Neural Networks

2007 ◽  
Author(s):  
Efstratios F. Georgopoulos ◽  
Sotiris M. Giannaropoulos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Resource Management ◽  
Optimization Problems ◽  
Artificial Neural ◽  
Management Optimization ◽  
Contact Centers

scholarly journals Framework for Slice-Aware Radio Resource Management Utilizing Artificial Neural Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 174972-174987
Author(s):  
Behnam Khodapanah ◽  
Ahmad Awada ◽  
Ingo Viering ◽  
Andre Noll Barreto ◽  
Meryem Simsek ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Resource Management ◽  
Radio Resource Management ◽  
Radio Resource ◽  
Artificial Neural

Artificial neural networks used in optimization problems

2018 ◽  
Vol 272 ◽  
pp. 10-16 ◽  
Author(s):  
Gabriel Villarrubia ◽  
Juan F. De Paz ◽  
Pablo Chamoso ◽  
Fernando De la Prieta
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optimization Problems ◽  
Artificial Neural

Training Artificial Neural Networks With Improved Particle Swarm Optimization

2020 ◽  
pp. 63-77
Author(s):  
Kuruge Darshana Abeyrathna ◽  
Chawalit Jeenanunta
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Artificial Neural Networks ◽  
Local Minimum ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Extra Step ◽  
Artificial Neural

Particle Swarm Optimization (PSO) is popular for solving complex optimization problems. However, it easily traps in local minima. Authors modify the traditional PSO algorithm by adding an extra step called PSO-Shock. The PSO-Shock algorithm initiates similar to the PSO algorithm. Once it traps in a local minimum, it is detected by counting stall generations. When stall generation accumulates to a prespecified value, particles are perturbed. This helps particles to find better solutions than the current local minimum they found. The behavior of PSO-Shock algorithm is studied using a known: Schwefel's function. With promising performance on the Schwefel's function, PSO-Shock algorithm is utilized to optimize the weights and bias of Artificial Neural Networks (ANNs). The trained ANNs then forecast electricity consumption in Thailand. The proposed algorithm reduces the forecasting error compared to the traditional training algorithms. The percentage reduction of error is 23.81% compared to the Backpropagation algorithm and 16.50% compared to the traditional PSO algorithm.

scholarly journals Optimal converter control for PV-fed DC and AC interconnection by using hybrid artificial neural networks

2020 ◽  
Author(s):  
Volkan Yamacli ◽  
Kadir Abaci
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power System ◽  
High Voltage ◽  
Power Converters ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Solution Space ◽  
System Stability ◽  
Artificial Neural

Abstract Optimal control of power converters to avoid voltage instability in cases such as system loading or faults is one of the most studied nonlinear problems that affect energy quality in power systems. The optimization problem related to converter control becomes more difficult with the inclusion of renewable energy systems while trying to fulfill power system constraints and providing an adequate amount of energy. In this paper, a simple approach based on artificial neural networks (ANNs) has been proposed and applied to photovoltaic-fed high-voltage DC and high-voltage AC systems interconnection consisting of PI-controlled power converters. By using the proposed method, converter control parameters are optimized for different cases to improve steady-state and dynamic voltage stability while also avoiding any kind of system faults. In order to implement hybrid control methodology by using ANN and PI control, the network should be well trained with samples including not only global best values but also the whole possible system characteristic. For this reason, a novel optimization algorithm, differential search algorithm, is used to sample solution space and train ANN by using random and localized samples. Obtained and presented results of the proposed approach show that due to robust and fast response, ANNs can be successfully used to overcome nonlinear security and optimization problems concerning power system stability.

Potential of artificial neural networks for resource scheduling

1997 ◽  
Vol 11 (3) ◽  
pp. 171-185 ◽  
Author(s):  
Nabil Kartam ◽  
Tanit Tongthong
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optimization Problems ◽  
Construction Project ◽  
Hopfield Network ◽  
Ann Model ◽  
Hopfield Networks ◽  
Resource Leveling ◽  
Artificial Neural ◽  
Optimum Solution

AbstractIn a construction project, resource leveling techniques are necessary as a primary schedule-improvement tool to reduce overall project cost by decreasing day-to-day fluctuation in resource usage and resource idleness. There are, however, some limitations in traditional resource leveling techniques. Conventional heuristic approaches cannot guarantee a near-optimum solution for every construction project; a given heuristic may perform well on one project and poorly on another. The existing optimization approaches, such as linear programming and enumeration methods, are best applicable only to small size problems. Recently, there has been success in the use of Artificial Neural Networks (ANNs) for solving some optimization problems. The paper discusses how state-of-the-art ANNs can be a functional alternative to traditional resource leveling techniques. It then investigates the application of different ANN models (such as backpropagation networks, Hopfield networks, Boltzmann machines, and competition ANNs) to resource leveling problems. Because the development of ANNs involves not only science but also experience, the paper presents various intuitive yet effective ways of mapping resource leveling problems on different applicable ANN architectures. To demonstrate the application of ANNs to resource leveling, a simple ANN model is developed using a Hopfield network. The conclusion highlights the usefulness and the limitations of ANNs when applied to resource leveling problems.

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