ScholarGate
助手

方法对比

并排查看您选择的方法;存在差异的行会高亮显示。

贝叶斯目标规划×目标规划×
领域仿真决策
方法族Process / pipelineMCDM
起源年份1990s1955
提出者Rios Insua, D. and colleaguesCharnes, A., Cooper, W. W.
类型Multi-objective optimization under uncertaintyMulti-objective optimisation — weighted/lexicographic goal deviation minimisation
开创性文献Rios Insua, D. (1990). Sensitivity Analysis in Multi-objective Decision Making. Springer-Verlag, Berlin. ISBN: 9783540528814Charnes, A., Cooper, W. W. (1955). Optimal estimation of executive compensation by linear programming. Management Science DOI ↗
别名BGP, Bayesian GP, Probabilistic Goal Programming, Bayesian Multi-Goal Optimization
相关68
摘要Bayesian Goal Programming (BGP) integrates Bayesian statistical inference with classic goal programming to handle uncertainty in targets and parameters. Instead of treating goal thresholds as fixed constants, BGP encodes them as probability distributions, updates beliefs using observed data, and then solves the resulting probabilistic optimization problem to find solutions that satisfy multiple aspirational goals under uncertainty.GOAL-PROGRAMMING (Goal Programming — Minimise deviations from multiple aspiration levels) is a ranking multi-criteria decision-making (MCDM) method introduced by Charnes, A., Cooper, W. W. in 1955. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
ScholarGate数据集
  1. v1
  2. 2 来源
  3. PUBLISHED
  1. v1
  2. 1 来源
  3. PUBLISHED

前往搜索 Download slides

ScholarGate方法对比: Bayesian Goal Programming · GOAL-PROGRAMMING. 于 2026-06-15 检索自 https://scholargate.app/zh/compare