เปรียบเทียบวิธี

	การวิเคราะห์สถานการณ์เชิงสุ่ม	Stochastic Dynamic Programming
สาขาวิชา	การจำลอง	การจำลอง
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1955–1980s	1957
ผู้ริเริ่ม	Dantzig, G. B.; Birge, J. R.; and others in stochastic programming tradition	Bellman, R.; formalized for stochastic settings by Puterman, M. L.
ประเภท	Probabilistic scenario enumeration and evaluation	Sequential optimization under uncertainty
แหล่งต้นตำรับ	Birge, J. R., Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer. ISBN: 9781461402374	Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093
ชื่อเรียกอื่น	Probabilistic Scenario Analysis, SSA, Stochastic What-If Analysis, Monte Carlo Scenario Analysis	SDP, Markov Decision Process, MDP, Stochastic DP
ที่เกี่ยวข้อง	4	6
สรุป	Stochastic Scenario Analysis evaluates a system or decision across multiple explicitly defined scenarios, each assigned a probability of occurrence. Unlike deterministic scenario analysis, it propagates uncertainty through probability distributions and computes expected outcomes, variance, and risk metrics across the scenario space, giving decision-makers a structured view of what could happen and how likely each outcome is.	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.