Optimization of cash management for ATM network

This paper presents an approach to cash management for automatic teller machine (ATM) network. This approach is based on an artificial neural network to forecast a daily cash demand for every ATM in the network and on the optimization procedure to estimate the optimal cash load for every ATM. During the optimization procedure the most important factors for ATMs maintenance were considered: cost of cash, cost of cash uploading and cost of daily services. Simulation studies show, that in case of higher cost of cash (interest rate) and lower cost for money uploading, the optimization procedure allows to decrease the ATMs maintenance costs around 15-20 %. For practical implementation of the proposed ATMs' cash management procedure, further experimental investigations are necessary.