Comparar métodos

	Programación Entera	Simheuristics
Campo	Optimización	Optimización
Familia	Process / pipeline	Process / pipeline
Año de origen	1958	2015
Autor original	Ralph Gomory (cutting planes, 1958); land-and-doig branch-and-bound (1960)	Juan et al.
Tipo	Mathematical optimisation — exact combinatorial method	Hybrid simulation-optimization framework
Fuente seminal	Wolsey, L.A. (1998). Integer Programming. Wiley. ISBN: 9780471283669	Juan, A. A., et al. (2015). A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems. Operations Research Perspectives, 2, 62–72.
Alias	IP, MIP, mixed-integer programming, mixed-integer linear programming	Simulation-based Metaheuristics, Stochastic Metaheuristics with Simulation, Hybrid Simulation-Optimization, Simülistik Sezgiseller
Relacionados	4	3
Resumen	Integer programming (IP), also called mixed-integer programming (MIP) when only some variables are restricted to whole numbers, is a branch of mathematical optimisation in which some or all decision variables must take integer or binary values. Building on linear programming, it was formalised through Ralph Gomory's cutting-plane method (1958) and the Land-and-Doig branch-and-bound algorithm (1960), and it has since become the standard exact framework for scheduling, assignment, routing, and resource-allocation problems.	Simheuristics is a hybrid algorithmic framework that integrates Monte Carlo or discrete-event simulation into metaheuristic search procedures to solve stochastic combinatorial optimization problems. Introduced by Juan et al. in 2015, it addresses settings where objective function evaluations involve random variables, providing near-optimal solutions with probabilistic quality guarantees. The approach is especially suited for real-world logistics, transportation, and scheduling problems where uncertainty is inherent and classical deterministic solvers fail to capture variability.