เปรียบเทียบวิธี

	การโปรแกรมจำนวนเต็มแบบสุ่ม	การโปรแกรมจำนวนเต็มผสม
สาขาวิชา	การจำลอง	การจำลอง
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1955	1958–1960
ผู้ริเริ่ม	Dantzig, G. B.; Beale, E. M. L.	Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960)
ประเภท	Optimization under uncertainty with discrete decisions	Mathematical optimization
แหล่งต้นตำรับ	Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York. ISBN: 978-1-4614-0237-4	Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432
ชื่อเรียกอื่น	SIP, Stochastic IP, Integer Stochastic Programming, Mixed-Integer Stochastic Programming	MIP, Mixed-Integer Linear Programming, MILP, Integer Programming
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.	Mixed-Integer Programming (MIP) is a mathematical optimization framework in which some decision variables must take integer values while others may be continuous. It generalizes linear programming and is widely used in operations research, logistics, scheduling, resource allocation, and engineering design, where indivisibility constraints — such as yes/no decisions or whole-unit quantities — arise naturally.