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We propose a new approach to accelerate the convergence of the modified policy iteration method for Markov decision processes with the total expected discounted reward. In the new policy iteration an additional operator is applied to the iterate generated by Markov operator, resulting in a bigger improvement in each iteration.
In the transshipment problem a number of retailers facing stochastic demand must place orders before the demand is known, but can transship inventory once the demand is realized. Recently developed simulation-based algorithm [1] provides near-optimal solutions, but can only handle small-to-medium problems. We develop an approximation-based approach where all transshipments are routed through a virtual...
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