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Title: | A dynamic ordering policy for a stochastic inventory problem with cash constraints |
Authors: | Chen, Z Rossi, R |
Keywords: | stochastic inventory;non-stationary demand;cash-flow constraint;(s, C(x), S) policy |
Issue Date: | 25-Nov-2020 |
Publisher: | Elsevier |
Citation: | Chen, Z. and Rossi, M. (2021) 'A dynamic ordering policy for a stochastic inventory problem with cash constraints', Omega, 102, 102378, pp. 1 - 14. doi: 10.1016/j.omega.2020.102378. |
Abstract: | This paper investigates a stochastic inventory management problem in which a cash-constrained small retailer periodically purchases a product and sells it to customers while facing non-stationary demand. In each period, the retailer’s available cash restricts the maximum quantity that can be ordered. There is a fixed ordering cost incurred when an order is issued by the retailer. We introduce a heuristic (s, C(x), S) policy inspired by numerical findings and by a structural analysis. The policy operates as follows: when the initial inventory x is less than s and the initial cash is greater than the state-dependent value C(x), the retailer should order a quantity that brings inventory as close to Sas possible; otherwise, the retailer should not order. We first determine the values of the controlling parameters s, C(x) and Svia the re- sults of stochastic dynamic programming and test their performance in an extensive computational study. The results show that the (s, C(x), S) policy performs well, with a maximum optimality gap of less than 1%, and an average gap of approximately 0.03%. We then develop a simple and time-efficient heuristic method for computing policy (s, C(x), S) by solving a mixed-integer linear programming problem: the average gap for this heuristic is less than 1% on our test bed. |
URI: | https://bura.brunel.ac.uk/handle/2438/28105 |
DOI: | https://doi.org/10.1016/j.omega.2020.102378 |
ISSN: | 0305-0483 |
Other Identifiers: | ORCID iD: Zhen Chen https://orcid.org/0000-0002-1619-3017 102378 |
Appears in Collections: | Brunel Business School Research Papers |
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