Bandingkan metode
Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.
| Pemrograman Linear× | Optimisasi Robust× | |
|---|---|---|
| Bidang | Optimasi | Optimasi |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | 1947 | 1970s theoretical roots; modern tractable form from late 1990s–2004 |
| Pencetus≠ | George B. Dantzig | Ben-Tal, El Ghaoui & Nemirovski (seminal book, 2009); Bertsimas & Sim (tractable polyhedral formulation, 2004) |
| Tipe≠ | Mathematical programming / continuous optimization | Mathematical programming framework |
| Sumber perintis≠ | Dantzig, G.B. (1963). Linear Programming and Extensions. Princeton University Press. ISBN: 9780691059136 | Ben-Tal, A., El Ghaoui, L. & Nemirovski, A. (2009). Robust Optimization. Princeton University Press. ISBN: 9780691143682 |
| Alias | LP, linear optimization, Doğrusal Programlama (LP) | minimax optimization, worst-case optimization, Gürbüz Optimizasyon (Robust Optimization) |
| Terkait≠ | 4 | 5 |
| Ringkasan≠ | Linear programming (LP), pioneered by George B. Dantzig in 1947, is a mathematical method for finding the best value of a linear objective function — such as minimum cost or maximum profit — subject to a set of linear inequality and equality constraints. It is the foundational technique in operations research and underlies production planning, resource allocation, logistics, diet problems, and countless other decision-making scenarios across engineering, economics, and the natural sciences. | Robust optimization is a mathematical programming framework, formalised by Ben-Tal and Nemirovski in the late 1990s and made broadly tractable by Bertsimas and Sim (2004), that finds decisions guaranteed to perform acceptably under every scenario within a predefined uncertainty set — rather than assuming parameter values are known exactly. Instead of optimising for a single expected outcome, it minimises the worst-case objective across all plausible realisations of uncertain data. |
| ScholarGateSet data ↗ |
|
|