Solving large-scale maximum expected covering location problems by genetic algorithms: A comparative study
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Bibliographic InfoArticle provided by Elsevier in its journal European Journal of Operational Research.
Volume (Year): 141 (2002)
Issue (Month): 3 (September)
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- J. Redondo & J. Fernández & I. García & P. Ortigosa, 2011. "Parallel algorithms for continuous multifacility competitive location problems," Journal of Global Optimization, Springer, vol. 50(4), pages 557-573, August.
- Rajagopalan, Hari K. & Vergara, F. Elizabeth & Saydam, Cem & Xiao, Jing, 2007. "Developing effective meta-heuristics for a probabilistic location model via experimental design," European Journal of Operational Research, Elsevier, vol. 177(1), pages 83-101, February.
- Geroliminis, Nikolas & Kepaptsoglou, Konstantinos & Karlaftis, Matthew G., 2011. "A hybrid hypercube - Genetic algorithm approach for deploying many emergency response mobile units in an urban network," European Journal of Operational Research, Elsevier, vol. 210(2), pages 287-300, April.
- Lee, Sang M. & Asllani, Arben A., 2004. "Job scheduling with dual criteria and sequence-dependent setups: mathematical versus genetic programming," Omega, Elsevier, vol. 32(2), pages 145-153, April.
- Rajagopalan, Hari K. & Saydam, Cem, 2009. "A minimum expected response model: Formulation, heuristic solution, and application," Socio-Economic Planning Sciences, Elsevier, vol. 43(4), pages 253-262, December.
- Noor-E-Alam, Md. & Mah, Andrew & Doucette, John, 2012. "Integer linear programming models for grid-based light post location problem," European Journal of Operational Research, Elsevier, vol. 222(1), pages 17-30.
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