Abstract
In this paper, we develop a theoretical framework for the common business practice of rolling horizon decision making. The main idea of our approach is that the usefulness of rolling horizon methods is, to a great extent, implied by the fact that forecasting the future is a costly activity. We, therefore, consider a general, discrete-time, stochastic dynamic optimization problem in which the decision maker has the possibility to obtain information on the uncertain future at given cost. For this non-standard optimization problem with optimal stopping decisions, we develop a dynamic programming formulation. We treat both finite and infinite horizon cases. We also provide a careful interpretation of the dynamic programming equations and illustrate our results by a simple numerical example. Various generalizations are shown to be captured by straightforward modifications of our model.
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This research is supported in part by NSERC Grant A4619, SSHRC Grant 410-87-0524, and Manufacturing Research Corporation of Ontario. Comments and suggestions from Qing Zhang and the referees are gratefully acknowledged.
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Sethi, S., Sorger, G. A theory of rolling horizon decision making. Ann Oper Res 29, 387–415 (1991). https://doi.org/10.1007/BF02283607
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DOI: https://doi.org/10.1007/BF02283607