विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| स्टोकेस्टिक लीनियर प्रोग्रामिंग× | स्टोकेस्टिक डायनामिक प्रोग्रामिंग× | |
|---|---|---|
| क्षेत्र | अनुकरण | अनुकरण |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 1955 | 1957 |
| प्रवर्तक≠ | George B. Dantzig | Bellman, R.; formalized for stochastic settings by Puterman, M. L. |
| प्रकार≠ | Stochastic optimization model | Sequential optimization under uncertainty |
| मौलिक स्रोत≠ | Dantzig, G. B., & Madansky, A. (1961). On the solution of two-stage linear programs under uncertainty. Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, 1, 165–176. link ↗ | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093 |
| उपनाम | SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP | SDP, Markov Decision Process, MDP, Stochastic DP |
| संबंधित≠ | 5 | 6 |
| सारांश≠ | Stochastic Linear Programming (SLP) extends classical linear programming to settings where some model parameters — costs, demands, resource availability — are uncertain and modeled as random variables. By optimizing expected costs over a probability distribution of scenarios, SLP produces decisions that remain feasible and near-optimal across a range of possible futures rather than for a single assumed state of the world. | Stochastic Dynamic Programming (SDP) is a mathematical optimization framework for sequential decision problems where outcomes are partly random. It extends Bellman's principle of optimality to stochastic environments, representing problems as Markov Decision Processes (MDPs) and computing optimal policies by solving recursive value equations over states and time periods. |
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