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| Байесов целеви програмен модел× | Многоцелева оптимизация× | |
|---|---|---|
| Област | Симулационно моделиране | Симулационно моделиране |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1990s | 1896 (concept); 1989–2002 (evolutionary algorithms era) |
| Създател≠ | Rios Insua, D. and colleagues | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. |
| Тип≠ | Multi-objective optimization under uncertainty | Optimization framework |
| Основополагащ източник≠ | Rios Insua, D. (1990). Sensitivity Analysis in Multi-objective Decision Making. Springer-Verlag, Berlin. ISBN: 9783540528814 | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| Други названия | BGP, Bayesian GP, Probabilistic Goal Programming, Bayesian Multi-Goal Optimization | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization |
| Свързани≠ | 6 | 3 |
| Резюме≠ | 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. | Multi-Objective Optimization (MOO) is a mathematical and computational framework for finding solutions that simultaneously optimize two or more conflicting objective functions. Rather than collapsing all goals into a single scalar, MOO produces a set of trade-off solutions — the Pareto front — from which a decision-maker selects according to preference. It is widely used in engineering design, operations research, logistics, economics, and policy analysis. |
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