Sammenlign metoder

	Deterministisk heltallsprogrammering – Eksakt optimering med faste parametere	Robust Mixed-Integer Programming
Fagfelt	Simulering	Simulering
Familie	Process / pipeline	Process / pipeline
Opprinnelsesår	1958–1960	1998–2004
Opphavsperson	Gomory, R. E.; Dantzig, G. B.; Land, A. H.; Doig, A. G.	Ben-Tal & Nemirovski; Bertsimas & Sim
Type	Mathematical programming / combinatorial optimization	Deterministic robust reformulation of MIP under uncertainty
Opprinnelig kilde	Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. John Wiley & Sons, New York. ISBN: 9780471359432	Bertsimas, D., Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53.
Alias	Deterministic MIP, Deterministic MILP/MIQP, Classical Mixed-Integer Programming, Deterministic MIP Optimization	RMIP, Robust MIP, Uncertain MIP, Robust MILP/MIQP
Relaterte	6	4
Sammendrag	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.	Robust Mixed-Integer Programming (RMIP) combines mixed-integer programming with robust optimization to find solutions that remain feasible and near-optimal despite uncertain parameters. Instead of assuming fixed data, it protects decisions against adversarial or worst-case realizations of uncertain inputs, using an explicit uncertainty set to control the degree of conservatism while preserving the combinatorial structure of integer decisions.