Comparer des méthodes

	Programmation par Contraintes Basée sur les Agents	Programmation Linéaire en Nombres Entiers Stochastique
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1990s–2000s	1955
Auteur d'origine	Emerged from multi-agent systems and operations research communities	Dantzig, G. B.; Beale, E. M. L.
Type	Hybrid simulation-optimization	Optimization under uncertainty with discrete decisions
Source fondatrice	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
Alias	ABIP, Agent-based IP, Multi-agent integer programming, ABM-IP	SIP, Stochastic IP, Integer Stochastic Programming, Mixed-Integer Stochastic Programming
Apparentées	3	6
Résumé	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.