A Stopping Rule for Forecasting Horizons in Nonhomogeneous Markov Decision Processes

James C. Bean, Wallace J. Hopp, Izak Duenyas

Operations Research · 1992 · Volume 40 · Issue 6

Abstract

We formulate a mixed integer program to determine whether a finite time horizon is a forecast horizon in a nonhomogeneous Markov decision process. We give a Bender's decomposition approach to solving this problem that evaluates the stopping rule, eliminates some suboptimal combinations of actions, and yields bounds on the maximum error that could result from the selection of a candidate action in the initial stage. The integer program arising from the decomposition has special properties that allow efficient solution. We illustrate the approach with numerical examples.

Keywords

dynamic programming/optimal control: Markov, finite states, solving nonhomogeneous problems, programming, integer: Bender's decomposition, applications to forecast horizons

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