A generative solution for ATM cash management

Abstract

Optimizing cash in automatic teller machines (ATM) is challenging due unpredictability of withdrawals, but profitable because of the large number of tellers. Generally ATM cash management and optimization is performed manually, according to corporate policies and personnel experience. A non-optimal cash upload can lead to poor service when cash demand is underestimated and to unnecessary costs when demand is overestimated. Therefore, finding the best match between cash stock and demand becomes crucial to improve. Recently, some authors attempted to optimize the cash by modeling and forecasting the demand. However, the high variance and non-stationarity of the underlying stochastic process can affect reliability of such an approach. In this paper we suggest the application of genetic algorithms as means for searching and generating optimal upload strategies, able at the same time to minimize the daily amount of stocked money and to assure cash dispensing service. Experimentation led at Poste Italiane S.p.A. makes this promising and worth to be further investigated.

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