On the use of genetic algorithms to solve location problems
By Jorge H. Jaramillo, J. Bhadury, Rajan Batta. Computers & Operations Research, 2002
321 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Industrial and Manufacturing Engineering, Organizational Behavior and Human Resource Management
View Paper: https://www.semanticscholar.org/paper/8ff59aae068195d00b561d171d1003312551a03f
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In Control and Systems Engineering
In Industrial and Manufacturing Engineering
In Organizational Behavior and Human Resource Management
Citations in the corpus
- Economic Optimization of a Lignocellulosic Biomass-to-Ethanol Supply Chain (2012, methodology): “For large problems, an approximate solution approach might be needed (see, for example, Gen and Cheng, 1997; Jaramillio et al., 2002; Hinojosa et al., 2000).”
- A multiperiod set covering location model for dynamic redeployment of ambulances (2008, background): “Because of the complex combinatorial nature of these problems, there have been various successful attempts to identify near-optimal solutions through the use of meta-heuristic search methods [33] such as; tabu search [10,34,35]; simulated annealing [31,36]; and evolutionary algorithms [8,9,37,38].”
- Artificial intelligence in supply chain management: theory and applications (2010, background): “…Chen et al. 1998, Park 2001); minimum spanning tree (Zhou and Gen 1998, 1999); delivery and pickup (Jung and Haghani 2000); bus network optimisation (Bielli et al. 2002); and location– allocation problems (Hosage and Goodchild 1986, Jaramillo et al. 2002, Zhou et al. 2002, 2003, Min et al. 2005).”