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Stochastické programování s cíli×Vícekriteriální cílové programování×
OborSimulaceSimulace
RodinaProcess / pipelineProcess / pipeline
Rok vzniku19681961
TvůrceContini, B. (building on Charnes & Cooper's chance-constrained programming)Charnes, A. and Cooper, W. W.
TypStochastic multi-goal optimizationMathematical programming / multi-criteria optimization
Původní zdrojContini, B. (1968). A stochastic approach to goal programming. Operations Research, 16(3), 576–586. DOI ↗Charnes, A., Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming. Wiley, New York. ISBN: 978-0471148258
Další názvySGP, Stochastic GP, Chance-Constrained Goal Programming, Probabilistic Goal ProgrammingMOGP, Multi-goal programming, Vector goal programming, Multi-criteria goal programming
Příbuzné64
ShrnutíStochastic 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.Multi-Objective Goal Programming (MOGP) is a mathematical programming technique that simultaneously pursues several aspirational targets by minimizing weighted deviations from each goal. Rooted in Charnes and Cooper's original goal programming framework (1961), MOGP extends it to handle multiple competing objectives, making it indispensable in operations research, supply chain design, resource allocation, and policy analysis where decision-makers must satisfy — or come close to — multiple conflicting requirements at once.
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ScholarGatePorovnat metody: Stochastic Goal Programming · Multi-objective goal programming. Získáno 2026-06-15 z https://scholargate.app/cs/compare