Á. A. Juan Pérez, J. Faulin Fajardo, L. Reyes, D. Raba, A. Freixes
Many combinatorial optimization problems (COPs) encountered in real-world logistics, transportation, production, financial, and telecommunication are NP-hard in nature. These real-life COPs are frequently characterized by their large-scale sizes and the need for obtaining high-quality solutions in short computing times, thus requiring the use of metaheuristic algorithms. Metaheuristics benefit from different random-search and parallelization paradigms, but they frequently assume that the problem inputs, the underlying objective function, and the set of optimization constraints are deterministic. However, uncertainty is all around us, which often makes deterministic models oversimplified versions of real-life systems. We describe a general methodology that allows for extending metaheuristics through simulation to solve stochastic COPs. ‘Simheuristics’ allow modelers for dealing with real-life uncertainty in a natural way by integrating simulation into a metaheuristic-driven framework.
Palabras clave / Keywords: heuristics, simulation
Programado
Sesión GT09-1: Optimización Heurística (HEURÍSTICA-1). Organizador: Juanjo Peiró
29 de mayo de 2018 17:00
Sala 6
