Abstract
A leader–follower facility problem is considered in this paper. The objective is to maximize the profit obtained by a chain (the leader) knowing that a competitor (the follower) will react by locating another single facility after the leader locates its own facility. A subpopulation-based evolutionary algorithm called TLUEGO was recently proposed to cope with this hard-to-solve global optimization problem. However, it requires high computational effort, even to manage small-size problems. In this work, three parallelizations of TLUEGO are proposed, a distributed memory programming algorithm, a shared memory programming algorithm, and a hybrid of the two previous algorithms, which not only allow us to obtain the solution faster, but also to solve larger instances.
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This work has been funded by grants from the Spanish Ministry of Science and Innovation (TIN2008-01117, ECO2008-00667/ECON), Junta de Andalucía (P10-TIC-6002, P11- TIC-7176), Program CEI from MICINN (PYR-2012-15 CEI BioTIC GENIL, CEB09-0010) and Fundación Séneca (The Agency of Science and Technology of the Region of Murcia, 00003/CS/10 and 15254/PI/10), in part financed by the European Regional Development Fund (ERDF).
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Arrondo, A.G., Redondo, J.L., Fernández, J. et al. Solving a leader–follower facility problem via parallel evolutionary approaches. J Supercomput 70, 600–611 (2014). https://doi.org/10.1007/s11227-014-1106-0
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DOI: https://doi.org/10.1007/s11227-014-1106-0