So sánh phương pháp

	Lập trình tuyến tính Bayes	Quy hoạch tuyến tính ngẫu nhiên
Lĩnh vực	Mô phỏng	Mô phỏng
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1970s–1980s	1955
Người khởi xướng	Integrated from Dantzig (LP) and Zellner/Bayesian econometrics traditions	George B. Dantzig
Loại	Optimization under Bayesian uncertainty	Stochastic optimization model
Công trình gốc	Dantzig, G. B. (1963). Linear Programming and Extensions. Princeton University Press, Princeton, NJ. ISBN: 9780691059136	Dantzig, G. B., & Madansky, A. (1961). On the solution of two-stage linear programs under uncertainty. Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, 1, 165–176.
Tên gọi khác	BLP, Bayesian LP, Bayesian stochastic linear programming, prior-posterior LP	SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP
Liên quan	6	5
Tóm tắt	Bayesian Linear Programming (BLP) integrates Bayesian statistical inference with classical linear programming to handle uncertainty in model parameters such as objective function coefficients, constraint coefficients, or right-hand-side values. Instead of treating parameters as fixed or governed by worst-case bounds, BLP uses prior beliefs updated by data to form posterior distributions, which then guide the LP formulation and solution, producing decisions that are optimal in a probabilistic, data-informed sense.	Stochastic Linear Programming (SLP) extends classical linear programming to settings where some model parameters — costs, demands, resource availability — are uncertain and modeled as random variables. By optimizing expected costs over a probability distribution of scenarios, SLP produces decisions that remain feasible and near-optimal across a range of possible futures rather than for a single assumed state of the world.