مقایسهٔ روش‌ها

	برنامه‌ریزی پویای سناریوی خط‌مشی	مدل مارکوف
حوزه	شبیه‌سازی	شبیه‌سازی
خانواده	Process / pipeline	Process / pipeline
سال پیدایش	1957	1906
پدیدآور	Bellman, Richard E.	Andrei Markov
نوع	Sequential optimization with scenario branching	Probabilistic state-transition model
منبع بنیادین	Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780691079516	Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963
نام‌های دیگر	PSDP, Policy-Scenario DP, Scenario-Based Dynamic Programming, Policy DP	Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process
مرتبط	5	5
خلاصه	Policy 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.	A Markov Model represents a system as a finite set of states and specifies the probability of moving from one state to another at each time step. By capturing only the current state — not the full history — it enables tractable analysis of complex dynamic processes across health economics, engineering reliability, operations research, and social-science modeling.