| Abstract [eng] |
The work presents self-service networks operational performance improvement and management system, which is adapted to manage the supply of ATMs’ cash flows. The system is created using multi-agent technologies and artificial neural networks. Adaptive neural network model has been created for the forecast of ATMs’ cash demand. Flexibility of the network is regulated in real time mode by limiting neural network weights, depending on process complexity, therefore such network better forecasts in real life situations. Evaluation and process improvement methodology has been created to optimize productivity of self-service systems, which includes: value-based self-service quality and performance criteria’s, and performance evaluation models. Using these models is possible to increase productivity of self-service systems. Using theoretical studies results a computer program enabling real-time monitoring and management of ATM cash flows was created. Analysis of high and low intensity of ATM network profitability showed that the created flexible neural network forecast method is more superior than classical methods of time series forecast (moving average, Holt, Winters, and ARMA), and is able to quite accurately forecast various time series of ATM cash demand. Based on studies found that using the created ANN method and optimization procedure, ATM cash management productivity may be approximately increased by 33 percent. |
