Improving cash logistics in bank branches by coupling machine learning and robust optimization

Abstract

This paper describes how Machine Learning and Robust Optimization techniques can greatly improve cash logistics operations. Specifically, we seek to optimize the logistics followed by the different branches of a given bank. Machine Learning is used to forecast cash demands for each of the branches, taking into account past demands and calendar effects. These demand predictions are forwarded to a Robust Optimization model, whose outputs are the cash transports that each branch should request. These transports guarantee that demand is fulfilled up to the desired confidence level, while also satisfying additional constraints arising in this particular domain.