Comparer des méthodes

	Programmation Linéaire en Nombres Entiers	Programmation stochastique à variables mixtes entières
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1958–1960	1990s–2000s
Auteur d'origine	Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960)	Birge, J. R.; Louveaux, F.; Sen, S.
Type	Mathematical optimization	Stochastic optimization model
Source fondatrice	Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432	Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer Series in Operations Research. New York: Springer. ISBN: 9780387982175
Alias	MIP, Mixed-Integer Linear Programming, MILP, Integer Programming	SMIP, Stochastic MIP, Mixed-Integer Stochastic Programming, SMILP
Apparentées	6	5
Résumé	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.	Stochastic Mixed-Integer Programming (SMIP) is an optimization framework that finds the best mix of binary, integer, and continuous decisions when key parameters — costs, demands, capacities — are uncertain and modeled as probability distributions over a set of scenarios. It extends classical MIP by embedding scenario trees or expected-value objectives that hedge against uncertainty while respecting combinatorial constraints.