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| Programowanie całkowitoliczbowe oparte na agentach× | Programowanie stochastyczne z ograniczeniami całkowitoliczbowymi× | |
|---|---|---|
| Dziedzina | Symulacja | Symulacja |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1990s–2000s | 1955 |
| Twórca≠ | Emerged from multi-agent systems and operations research communities | Dantzig, G. B.; Beale, E. M. L. |
| Typ≠ | Hybrid simulation-optimization | Optimization under uncertainty with discrete decisions |
| Źródło pierwotne≠ | Wooldridge, M. (2009). An Introduction to MultiAgent Systems (2nd ed.). Wiley. ISBN: 9780470519462 | Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York. ISBN: 978-1-4614-0237-4 |
| Inne nazwy | ABIP, Agent-based IP, Multi-agent integer programming, ABM-IP | SIP, Stochastic IP, Integer Stochastic Programming, Mixed-Integer Stochastic Programming |
| Pokrewne≠ | 3 | 6 |
| Podsumowanie≠ | Agent-Based Integer Programming (ABIP) couples the behavioral richness of agent-based modeling with the combinatorial rigor of integer programming. Individual agents pursue local objectives while a global IP solver enforces discrete feasibility constraints, enabling realistic modeling of multi-actor systems where decisions must be integer-valued — such as resource allocation, scheduling, and network design under emergent interaction effects. | Stochastic Integer Programming (SIP) is an optimization framework that combines integer (discrete) decision variables with explicit probabilistic modeling of uncertainty. It seeks the best here-and-now decision that minimizes expected cost (or maximizes expected benefit) across a distribution of future scenarios, accounting for the fact that some decisions must be made before uncertainty is resolved. |
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