Linganisha mbinu

	Bayesian Mixed-Integer Programming	Uchambuzi wa Programu Mchanganyiko-Nusu-Imara (Robust Mixed-Integer Programming)
Nyanja	Uigaji	Uigaji
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2018 (surrogate-BO-MIP synthesis); MIP foundations 1958	1998–2004
Mwanzilishi	Baptista, R. & Poloczek, M. (formal Bayesian-BO-MIP formulation); mixed-integer programming roots in Gomory (1958)	Ben-Tal & Nemirovski; Bertsimas & Sim
Aina	Surrogate-assisted combinatorial optimization	Deterministic robust reformulation of MIP under uncertainty
Chanzo asilia	Baptista, R., Poloczek, M. (2018). Bayesian Optimization of Combinatorial Structures. Proceedings of the 35th International Conference on Machine Learning (ICML), PMLR 80:462–471.	Bertsimas, D., Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53.
Majina mbadala	Bayesian MIP, BO-MIP, Bayesian Combinatorial Optimization, Mixed-Integer Bayesian Optimization	RMIP, Robust MIP, Uncertain MIP, Robust MILP/MIQP
Zinazohusiana	5	4
Muhtasari	Bayesian Mixed-Integer Programming (BO-MIP) couples a probabilistic surrogate model — typically a Gaussian process — with a mixed-integer programming solver to efficiently optimize expensive black-box objectives defined over spaces that contain both continuous and discrete or integer-valued decision variables. It is especially valuable when each function evaluation is costly and exhaustive search is infeasible.	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.