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http://hdl.handle.net/10609/90873
Título : | Combining biased random sampling with metaheuristics for the facility location problem in distributed computer systems |
Autoría: | Cabrera Añon, Guillem González Martín, Sergio Juan, Angel A. Grasman, Scott Erwin Marquès Puig, Joan Manuel |
Citación : | Cabrera, G., Gonzalez-Martin, S., Juan, A.A., Marquès, J.M. & Grasman, S.E. (2014). Combining biased random sampling with metaheuristics for the facility location problem in distributed computer systems. Winter Simulation Conference (WSC). Proceedings, 2014(), 3000-3011. doi: 10.1109/WSC.2014.7020139 |
Resumen : | This paper introduces a probabilistic algorithm for solving the well-known Facility Location Problem (FLP), an optimization problem frequently encountered in practical applications in fields such as Logistics or Telecommunications. Our algorithm is based on the combination of biased random sampling -using a skewed probability distribution- with a metaheuristic framework. The use of random variates from a skewed distribution allows to guide the local search process inside the metaheuristic framework which, being a stochastic procedure, is likely to produce slightly different results each time it is run. Our approach is validated against some classical benchmarks from the FLP literature and it is also used to analyze the deployment of service replicas in a realistic Internet-distributed system. |
Palabras clave : | localización de instalaciones algoritmos aleatorios métodos de muestreo distribución de probabilidad procesos estocásticos internet |
DOI: | 10.1109/WSC.2014.7020139 |
Tipo de documento: | info:eu-repo/semantics/conferenceObject |
Fecha de publicación : | dic-2014 |
Aparece en las colecciones: | Articles |
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