Comparer des méthodes

	Programmation en nombres entiers bayésienne	Optimisation bayésienne multi-objectifs
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1990s–2000s	2006-2016
Auteur d'origine	Baptiste, Lassagne, Nuijten and others in Bayesian optimization community	Emmerich, M.; Svenson, J.; and related Gaussian process optimization community
Type	Probabilistic combinatorial optimization	Surrogate-model-assisted multi-objective optimizer
Source fondatrice	Baptiste, P., Lassagne, I., & Nuijten, W. (2001). Bayesian reasoning in mixed integer programming. European Journal of Operational Research, 130(2), 293–313.	Svenson, J., Santner, T. (2016). Multiobjective optimization of expensive-to-evaluate deterministic computer simulator models. Computational Statistics & Data Analysis, 94, 250-264.
Alias	BIP, Bayesian combinatorial optimization, Bayesian discrete optimization, probabilistic integer programming	BMOO, Bayesian MOO, Multi-objective Bayesian optimization, MOBO
Apparentées	6	3
Résumé	Bayesian Integer Programming (BIP) integrates Bayesian probabilistic reasoning with integer programming to solve combinatorial optimization problems under uncertainty. Instead of treating parameters as fixed, it encodes prior beliefs about uncertain coefficients and updates them with observed data, producing a posterior-guided search over integer-feasible solutions. The approach is widely used in scheduling, resource allocation, and supply-chain planning where data are incomplete or noisy.	Bayesian Multi-Objective Optimization (BMOO/MOBO) uses Gaussian process surrogate models to approximate multiple expensive objective functions and guides the search toward the Pareto frontier with minimal real evaluations. By quantifying prediction uncertainty at each candidate point, it balances exploration of unknown regions against exploitation of promising solutions, making it especially powerful when each function evaluation is computationally or experimentally costly.