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Philipp Melchiors
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Philipp Melchiors: Technische Universität München

Chapter Chapter 3 in Dynamic and Stochastic Multi-Project Planning, 2015, pp 19-28 from Springer

Abstract: Abstract Dynamic programming is a general technique for solving sequential problems. The first comprehensive books on the topic have been written by Bellman [13] and Howard [62]. The most important methodologies for determining an optimal policy for a Markov decision process (MDP) are backward induction, value iteration (VI), policy iteration (PI) and linear programming. As MDPs are in discrete time where transitions have the same deterministic durations many results and methodologies, such as VI, cannot be directly applied to continuous-time Markov decision processes with exponentially distributed transition times.

Keywords: Markov Decision Process; Project Schedule; Policy Iteration; Approximate Dynamic Programming; Order Acceptance (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnechp:978-3-319-04540-5_3

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DOI: 10.1007/978-3-319-04540-5_3

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