Developing effective meta-heuristics for a probabilistic location model via experimental design

Developing effective meta-heuristics for a probabilistic location model via experimental design

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Article ID: iaor20084287
Country: Netherlands
Volume: 177
Issue: 1
Start Page Number: 83
End Page Number: 101
Publication Date: Feb 2007
Journal: European Journal of Operational Research
Authors: , , ,
Keywords: heuristics
Abstract:

This article employs a statistical experimental design to guide and evaluate the development of four meta-heuristics applied to a probabilistic location model. The meta-heuristics evaluated include evolutionary algorithm, tabu search, simulated annealing, and a hybridized hill-climbing algorithm. Comparative results are analyzed using ANOVA. Our findings show that all four implementations produce high quality solutions. In particular, it was found that on average tabu search and simulated annealing find their best solutions in the least amount of time, with relatively small variability. This is especially important for large-size problems when dynamic redeployment is required.

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