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Programmation en nombres entiers robuste×Programmation en nombres entiers×
DomaineSimulationOptimisation
FamilleProcess / pipelineProcess / pipeline
Année d'origine20031958
Auteur d'origineBertsimas, D. and Sim, M.Ralph Gomory (cutting planes, 1958); land-and-doig branch-and-bound (1960)
TypeDeterministic robust optimization with integer variablesMathematical optimisation — exact combinatorial method
Source fondatriceBertsimas, D., Sim, M. (2003). Robust discrete optimization and network flows. Mathematical Programming, 98(1-3), 49-71. DOI ↗Wolsey, L.A. (1998). Integer Programming. Wiley. ISBN: 9780471283669
AliasRIP, Robust IP, Robust Combinatorial Optimization, Integer Robust OptimizationIP, MIP, mixed-integer programming, mixed-integer linear programming
Apparentées64
RésuméRobust Integer Programming (RIP) finds integer or binary solutions that remain feasible and near-optimal across all scenarios in a prescribed uncertainty set. Rather than assuming exact knowledge of data, RIP hedges against the worst-case realization of uncertain costs or constraint coefficients, delivering decisions that are guaranteed to perform well even when inputs deviate from their nominal values.Integer programming (IP), also called mixed-integer programming (MIP) when only some variables are restricted to whole numbers, is a branch of mathematical optimisation in which some or all decision variables must take integer or binary values. Building on linear programming, it was formalised through Ralph Gomory's cutting-plane method (1958) and the Land-and-Doig branch-and-bound algorithm (1960), and it has since become the standard exact framework for scheduling, assignment, routing, and resource-allocation problems.
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ScholarGateComparer des méthodes: Robust Integer Programming · Integer Programming. Consulté le 2026-06-15 sur https://scholargate.app/fr/compare