방법 비교

	정수 계획법(IP) 및 혼합 정수 계획법(MIP)	시뮬레이션과 메타휴리스틱을 결합한 확률적 최적화를 위한 심휴리스틱스(Simheuristics)
분야	최적화	최적화
계열	Process / pipeline	Process / pipeline
기원 연도	1958	2015
창시자	Ralph Gomory (cutting planes, 1958); land-and-doig branch-and-bound (1960)	Juan et al.
유형	Mathematical optimisation — exact combinatorial method	Hybrid simulation-optimization framework
원전	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.
별칭	IP, MIP, mixed-integer programming, mixed-integer linear programming	Simulation-based Metaheuristics, Stochastic Metaheuristics with Simulation, Hybrid Simulation-Optimization, Simülistik Sezgiseller
관련	4	3
요약	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.