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Policy Scenario Dynamic Programming×Pemrograman Dinamis Stokastik×
BidangSimulasiSimulasi
KeluargaProcess / pipelineProcess / pipeline
Tahun asal19571957
PencetusBellman, Richard E.Bellman, R.; formalized for stochastic settings by Puterman, M. L.
TipeSequential optimization with scenario branchingSequential optimization under uncertainty
Sumber perintisBellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780691079516Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093
AliasPSDP, Policy-Scenario DP, Scenario-Based Dynamic Programming, Policy DPSDP, Markov Decision Process, MDP, Stochastic DP
Terkait56
RingkasanPolicy Scenario Dynamic Programming (PSDP) applies Bellman's recursive optimization framework to a set of pre-specified policy scenarios, enabling decision-makers to compare staged, sequential decisions under distinct future conditions. It decomposes a complex, multi-period policy choice into tractable sub-problems solved backward through time, yielding optimal action sequences for each scenario and a structured basis for scenario comparison.Stochastic Dynamic Programming (SDP) is a mathematical optimization framework for sequential decision problems where outcomes are partly random. It extends Bellman's principle of optimality to stochastic environments, representing problems as Markov Decision Processes (MDPs) and computing optimal policies by solving recursive value equations over states and time periods.
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ScholarGateBandingkan metode: Policy Scenario Dynamic Programming · Stochastic Dynamic Programming. Diakses 2026-06-15 dari https://scholargate.app/id/compare