Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Deterministische Dynamische Programmering× | Mixed-Integer Programming× | |
|---|---|---|
| Vakgebied | Simulatie | Simulatie |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 1957 | 1958–1960 |
| Grondlegger≠ | Richard E. Bellman | Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960) |
| Type≠ | Exact sequential optimization algorithm | Mathematical optimization |
| Oorspronkelijke bron≠ | Bellman, R. E. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780691079516 | Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432 |
| Aliassen | DDP, Deterministic DP, Classical Dynamic Programming, Bellman Dynamic Programming | MIP, Mixed-Integer Linear Programming, MILP, Integer Programming |
| Verwant | 6 | 6 |
| Samenvatting≠ | 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. | Mixed-Integer Programming (MIP) is a mathematical optimization framework in which some decision variables must take integer values while others may be continuous. It generalizes linear programming and is widely used in operations research, logistics, scheduling, resource allocation, and engineering design, where indivisibility constraints — such as yes/no decisions or whole-unit quantities — arise naturally. |
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