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http://hdl.handle.net/10609/90873
Title: Combining biased random sampling with metaheuristics for the facility location problem in distributed computer systems
Author: Cabrera Añon, Guillem  
González Martín, Sergio
Juan Pérez, Ángel Alejandro
Grasman, Scott Erwin
Marquès Puig, Joan Manuel
Keywords: facility location
internet
randomised algorithms
sampling methods
statistical distributions
stochastic processes
Issue Date: Dec-2014
Publisher: Winter Simulation Conference (WSC). Proceedings
Citation: 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
Series/Report no.: Winter Simulation Conference, Savannah, EUA, 7-10, desembre de 2014
Project identifier: info:eu-repo/grantAgreement/TRA2013-48180-C3-3-P
Also see: https://ieeexplore.ieee.org/document/7020139
https://informs-sim.org/wsc14papers/includes/files/269.pdf
Abstract: 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.
Language: English
URI: http://hdl.handle.net/10609/90873
ISBN: 9781479974863
ISSN: 1558-4305MIAR
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