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The Stochastic Cash Balance Problem with Fixed Costs for Increases and Decreases

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  • Edwin H. Neave

    (Northwestern University)

Abstract

The stochastic cash balance problem is an inventory problem in which the stochastic cash (or inventory) change can either be positive or nonpositive, and in which decisions to increase or decrease the inventory are permitted at the beginning of each time period. The paper studies problems in which both fixed and proportional costs can be incurred whenever the inventory is changed in either direction. An example is used to demonstrate that when these costs are positive and the loss function is convex, a simple policy (analogous to a two-sided (s, S) policy) is not generally optimal. The example is also used to display the relations between the cash balance problem and inventory problems previously studied by Scarf and Veinott. When proportional costs of changing the inventory are zero, the two fixed costs are equal, the loss function is symmetric quasi-convex, and the problem's probability densities are quasi-concave a simple policy is shown to be optimal. For the cases in which simple policies are not optimal, the paper develops a technique which employs convex upper and lower bounds on the (nonconvex) cost functions partially to describe the optimal policy. It is suggested that this convex bounding technique may provide an approach to studying the cost implications of following simple, nonoptimal policies in inventory problems for which the optimal policy is complex.

Suggested Citation

  • Edwin H. Neave, 1970. "The Stochastic Cash Balance Problem with Fixed Costs for Increases and Decreases," Management Science, INFORMS, vol. 16(7), pages 472-490, March.
  • Handle: RePEc:inm:ormnsc:v:16:y:1970:i:7:p:472-490
    DOI: 10.1287/mnsc.16.7.472
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    Cited by:

    1. Awi Federgruen & Zhe Liu & Lijian Lu, 2020. "Synthesis and Generalization of Structural Results in Inventory Management: A Generalized Convexity Property," Mathematics of Operations Research, INFORMS, vol. 45(2), pages 547-575, May.
    2. Alvarez, Fernando & Lippi, Francesco, 2013. "The demand of liquid assets with uncertain lumpy expenditures," Journal of Monetary Economics, Elsevier, vol. 60(7), pages 753-770.
    3. Frank Milne & Edwin H. Neave, 2003. "A General Equilibrium Financial Asset Economy With Transaction Costs And Trading Constraints," Working Paper 1082, Economics Department, Queen's University.
    4. Jianjun Xu & Mustafa Cagri Gürbüz & Youyi Feng & Shaoxiang Chen, 2020. "Optimal Spot Trading Integrated with Quantity Flexibility Contracts," Production and Operations Management, Production and Operations Management Society, vol. 29(6), pages 1532-1549, June.
    5. Awi Federgruen & Nan Yang, 2014. "Infinite Horizon Strategies for Replenishment Systems with a General Pool of Suppliers," Operations Research, INFORMS, vol. 62(1), pages 141-159, February.
    6. Eugene A. Feinberg & Mark E. Lewis, 2007. "Optimality Inequalities for Average Cost Markov Decision Processes and the Stochastic Cash Balance Problem," Mathematics of Operations Research, INFORMS, vol. 32(4), pages 769-783, November.
    7. Ben A. Chaouch, 2018. "Analysis of the stochastic cash balance problem using a level crossing technique," Annals of Operations Research, Springer, vol. 271(2), pages 429-444, December.
    8. Francisco Salas-Molina & Juan A. Rodríguez-Aguilar & Montserrat Guillen, 2023. "A multidimensional review of the cash management problem," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-35, December.
    9. Frank Milne, 2008. "Credit Crises, Risk Management Systems and Liquidity Modelling," Working Papers 1, John Deutsch Institute for the Study of Economic Policy.
    10. Marcos Melo & Feruccio Bilich, 2013. "Expectancy balance model for cash flow," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 37(2), pages 240-252, April.
    11. Premachandra, I. M., 2004. "A diffusion approximation model for managing cash in firms: An alternative approach to the Miller-Orr model," European Journal of Operational Research, Elsevier, vol. 157(1), pages 218-226, August.
    12. Neil Geismar & Milind Dawande & Divakar Rajamani & Chelliah Sriskandarajah, 2007. "Managing a Bank's Currency Inventory Under New Federal Reserve Guidelines," Manufacturing & Service Operations Management, INFORMS, vol. 9(2), pages 147-167, March.
    13. Hinderer, K. & Waldmann, K. -H., 2001. "Cash management in a randomly varying environment," European Journal of Operational Research, Elsevier, vol. 130(3), pages 468-485, May.
    14. Francisco Salas-Molina & David Pla-Santamaria & Juan A. Rodriguez-Aguilar, 2018. "A multi-objective approach to the cash management problem," Annals of Operations Research, Springer, vol. 267(1), pages 515-529, August.
    15. Qing Ye & Izak Duenyas, 2007. "Optimal Capacity Investment Decisions with Two-Sided Fixed-Capacity Adjustment Costs," Operations Research, INFORMS, vol. 55(2), pages 272-283, April.
    16. John A. Buzacott & Rachel Q. Zhang, 2004. "Inventory Management with Asset-Based Financing," Management Science, INFORMS, vol. 50(9), pages 1274-1292, September.
    17. Sandun C. Perera & Suresh P. Sethi, 2023. "A survey of stochastic inventory models with fixed costs: Optimality of (s, S) and (s, S)‐type policies—Discrete‐time case," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 131-153, January.
    18. Yonit Barron, 2022. "A probabilistic approach to the stochastic fluid cash management balance problem," Annals of Operations Research, Springer, vol. 312(2), pages 607-645, May.
    19. Sandun Perera & Winston Buckley & Hongwei Long, 2018. "Market-reaction-adjusted optimal central bank intervention policy in a forex market with jumps," Annals of Operations Research, Springer, vol. 262(1), pages 213-238, March.
    20. Sandun C. Perera & Suresh P. Sethi, 2023. "A survey of stochastic inventory models with fixed costs: Optimality of (s, S) and (s, S)‐type policies—Continuous‐time case," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 154-169, January.

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