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A stochastic programming approach to cash management in banking


  • Castro, Jordi


The treasurer of a bank is responsible for the cash management of several banking activities. In this work, we focus on two of them: cash management in automatic teller machines (ATMs), and in the compensation of credit card transactions. In both cases a decision must be taken according to a future customers demand, which is uncertain. From historical data we can obtain a discrete probability distribution of this demand, which allows the application of stochastic programming techniques. We present stochastic programming models for each problem. Two short-term and one mid-term models are presented for ATMs. The short-term model with fixed costs results in an integer problem which is solved by a fast (i.e. linear running time) algorithm. The short-term model with fixed and staircase costs is solved through its MILP equivalent deterministic formulation. The mid-term model with fixed and staircase costs gives rise to a multi-stage stochastic problem, which is also solved by its MILP deterministic equivalent. The model for compensation of credit card transactions results in a closed form solution. The optimal solutions of those models are the best decisions to be taken by the bank, and provide the basis for a decision support system.

Suggested Citation

  • Castro, Jordi, 2009. "A stochastic programming approach to cash management in banking," European Journal of Operational Research, Elsevier, vol. 192(3), pages 963-974, February.
  • Handle: RePEc:eee:ejores:v:192:y:2009:i:3:p:963-974

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    References listed on IDEAS

    1. J. G. Kallberg & R. W. White & W. T. Ziemba, 1982. "Short Term Financial Planning under Uncertainty," Management Science, INFORMS, vol. 28(6), pages 670-682, June.
    2. Willem Klein Haneveld & Maarten van der Vlerk, 1999. "Stochastic integer programming:General models and algorithms," Annals of Operations Research, Springer, vol. 85(0), pages 39-57, January.
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    Cited by:

    1. Wong, Man Hong, 2013. "Investment models based on clustered scenario trees," European Journal of Operational Research, Elsevier, vol. 227(2), pages 314-324.
    2. Alaeddine Faleh, 2011. "Un modèle de programmation stochastique pour l'allocation stratégique d'actifs d'un régime de retraite partiellement provisionné," Working Papers hal-00561965, HAL.
    3. Venkatesh, Kamini & Ravi, Vadlamani & Prinzie, Anita & Poel, Dirk Van den, 2014. "Cash demand forecasting in ATMs by clustering and neural networks," European Journal of Operational Research, Elsevier, vol. 232(2), pages 383-392.
    4. V. Kamini & V. Ravi & A. Prinzie & D. Van Den Poel, 2013. "Cash Demand Forecasting in ATMs by Clustering and Neural Networks," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/865, Ghent University, Faculty of Economics and Business Administration.


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