Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Baijesa mērķprogramēšana× | Stohastiskā mērķprogramēšana× | |
|---|---|---|
| Nozare | Simulācija | Simulācija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1990s | 1968 |
| Autors≠ | Rios Insua, D. and colleagues | Contini, B. (building on Charnes & Cooper's chance-constrained programming) |
| Tips≠ | Multi-objective optimization under uncertainty | Stochastic multi-goal optimization |
| Pirmavots≠ | Rios Insua, D. (1990). Sensitivity Analysis in Multi-objective Decision Making. Springer-Verlag, Berlin. ISBN: 9783540528814 | Contini, B. (1968). A stochastic approach to goal programming. Operations Research, 16(3), 576–586. DOI ↗ |
| Citi nosaukumi | BGP, Bayesian GP, Probabilistic Goal Programming, Bayesian Multi-Goal Optimization | SGP, Stochastic GP, Chance-Constrained Goal Programming, Probabilistic Goal Programming |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | 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. | Stochastic Goal Programming (SGP) extends classical goal programming to handle uncertainty in goal targets, constraint coefficients, or right-hand-side parameters. By incorporating probabilistic constraints and stochastic objective components, it finds solutions that satisfy multiple goals at acceptable probability levels, making it suitable for decision problems where data are inherently uncertain or variable. |
| ScholarGateDatu kopa ↗ |
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