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Bayesianische Zielprogrammierung×Zielprogrammierung×
FachgebietSimulationEntscheidungsfindung
FamilieProcess / pipelineMCDM
Entstehungsjahr1990s1955
UrheberRios Insua, D. and colleaguesCharnes, A., Cooper, W. W.
TypMulti-objective optimization under uncertaintyMulti-objective optimisation — weighted/lexicographic goal deviation minimisation
Wegweisende QuelleRios 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 ↗
AliasnamenBGP, Bayesian GP, Probabilistic Goal Programming, Bayesian Multi-Goal Optimization
Verwandt68
ZusammenfassungBayesian 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.
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ScholarGateMethoden vergleichen: Bayesian Goal Programming · GOAL-PROGRAMMING. Abgerufen am 2026-06-15 von https://scholargate.app/de/compare