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Programarea Stocastică a Obiectivelor×Programare cu obiective țintă multiple×
DomeniuSimulareSimulare
FamilieProcess / pipelineProcess / pipeline
Anul apariției19681961
Autorul originalContini, B. (building on Charnes & Cooper's chance-constrained programming)Charnes, A. and Cooper, W. W.
TipStochastic multi-goal optimizationMathematical programming / multi-criteria optimization
Sursa seminalăContini, 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
Denumiri alternativeSGP, Stochastic GP, Chance-Constrained Goal Programming, Probabilistic Goal ProgrammingMOGP, Multi-goal programming, Vector goal programming, Multi-criteria goal programming
Înrudite64
RezumatStochastic 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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  1. v1
  2. 2 Surse
  3. PUBLISHED

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ScholarGateCompară metode: Stochastic Goal Programming · Multi-objective goal programming. Preluat la 2026-06-15 de pe https://scholargate.app/ro/compare