Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Genetisch Algoritme× | Goal Programming× | Mixed-Integer Programming× | |
|---|---|---|---|
| Vakgebied≠ | Optimalisatie | Besluitvorming | Simulatie |
| Familie≠ | Process / pipeline | MCDM | Process / pipeline |
| Jaar van ontstaan≠ | 1975 | 1955 | 1958–1960 |
| Grondlegger≠ | John Henry Holland | Charnes, A., Cooper, W. W. | Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960) |
| Type≠ | Population-based metaheuristic | Multi-objective optimisation — weighted/lexicographic goal deviation minimisation | Mathematical optimization |
| Oorspronkelijke bron≠ | Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗ | Charnes, A., Cooper, W. W. (1955). Optimal estimation of executive compensation by linear programming. Management Science DOI ↗ | Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432 |
| Aliassen≠ | GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon | — | MIP, Mixed-Integer Linear Programming, MILP, Integer Programming |
| Verwant≠ | 5 | 8 | 6 |
| Samenvatting≠ | A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail. | GOAL-PROGRAMMING (Goal Programming — Minimise deviations from multiple aspiration levels) is a ranking multi-criteria decision-making (MCDM) method introduced by Charnes, A., Cooper, W. W. in 1955. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. | 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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