Comparer des méthodes

	Programmation Linéaire en Nombres Entiers Déterministe	Programmation Linéaire en Nombres Entiers
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1958–1960	1958–1960
Auteur d'origine	Gomory, R. E.; Dantzig, G. B.; Land, A. H.; Doig, A. G.	Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960)
Type	Mathematical programming / combinatorial optimization	Mathematical optimization
Source fondatrice	Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. John Wiley & Sons, New York. ISBN: 9780471359432	Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432
Alias	Deterministic MIP, Deterministic MILP/MIQP, Classical Mixed-Integer Programming, Deterministic MIP Optimization	MIP, Mixed-Integer Linear Programming, MILP, Integer Programming
Apparentées	6	6
Résumé	Deterministic Mixed-Integer Programming (MIP) is a mathematical optimization framework that finds the provably optimal solution to problems involving both continuous and integer decision variables under fully known, fixed coefficients and constraints. It is the foundational workhorse of operations research when all data are treated as certain.	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.