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| Deterministische Gemischt-Ganzzahlige Programmierung× | Deterministische Dynamische Programmierung× | |
|---|---|---|
| Fachgebiet | Simulation | Simulation |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1958–1960 | 1957 |
| Urheber≠ | Gomory, R. E.; Dantzig, G. B.; Land, A. H.; Doig, A. G. | Richard E. Bellman |
| Typ≠ | Mathematical programming / combinatorial optimization | Exact sequential optimization algorithm |
| Wegweisende Quelle≠ | Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. John Wiley & Sons, New York. ISBN: 9780471359432 | Bellman, R. E. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780691079516 |
| Aliasnamen | Deterministic MIP, Deterministic MILP/MIQP, Classical Mixed-Integer Programming, Deterministic MIP Optimization | DDP, Deterministic DP, Classical Dynamic Programming, Bellman Dynamic Programming |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | Deterministic Mixed-Integer Programming (MIP) is a mathematical optimization framework that finds the provably optimal solution to problems involving both continuous and integer decision variables under fully known, fixed coefficients and constraints. It is the foundational workhorse of operations research when all data are treated as certain. | Deterministic Dynamic Programming (DDP) is a mathematical optimization technique that decomposes a multi-stage decision problem into a sequence of simpler subproblems, solving them exactly when all system parameters — transition functions, costs, and rewards — are known with certainty. It guarantees a globally optimal policy via Bellman's principle of optimality. |
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