Abstract
We investigate the impact of the sample size in the non-stationary newsvendor problem when the underlying demand distribution is not known, and performance is measured by the decision-maker’s average regret. The approach we propose is entirely data-driven, in the sense that we do not estimate the probability distribution of the demand and instead rely exclusively on historical data. We propose an iterative algorithm to determine the number of past observations that should be included in the decision-making process, provide insights into the optimal sample size and perform extensive computational experiments.
Research partially supported by the National Science Foundation, grant DMI-0540143.
Research partially supported by the National Science Foundation, grant DMI-0540143. Corresponding author.
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Metan, G., Thiele, A. (2007). An Adaptive Algorithm for the Optimal Sample Size in the Non-Stationary Data-Driven Newsvendor Problem. In: Baker, E.K., Joseph, A., Mehrotra, A., Trick, M.A. (eds) Extending the Horizons: Advances in Computing, Optimization, and Decision Technologies. Operations Research/Computer Science Interfaces Series, vol 37. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-48793-9_6
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DOI: https://doi.org/10.1007/978-0-387-48793-9_6
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