เปรียบเทียบวิธี

	การโปรแกรมจำนวนเต็มแบบสุ่ม	Stochastic Dynamic Programming
สาขาวิชา	การจำลอง	การจำลอง
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1955	1957
ผู้ริเริ่ม	Dantzig, G. B.; Beale, E. M. L.	Bellman, R.; formalized for stochastic settings by Puterman, M. L.
ประเภท	Optimization under uncertainty with discrete decisions	Sequential optimization under uncertainty
แหล่งต้นตำรับ	Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York. ISBN: 978-1-4614-0237-4	Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093
ชื่อเรียกอื่น	SIP, Stochastic IP, Integer Stochastic Programming, Mixed-Integer Stochastic Programming	SDP, Markov Decision Process, MDP, Stochastic DP
ที่เกี่ยวข้อง	6	6
สรุป	Stochastic Integer Programming (SIP) is an optimization framework that combines integer (discrete) decision variables with explicit probabilistic modeling of uncertainty. It seeks the best here-and-now decision that minimizes expected cost (or maximizes expected benefit) across a distribution of future scenarios, accounting for the fact that some decisions must be made before uncertainty is resolved.	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.