On The Use of Genetic Algorithms to Solve Location Problems

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Joyendu Bhadury, Associate Dean - Graduate Programs and Research (Creator)
The University of North Carolina at Greensboro (UNCG )
Web Site: http://library.uncg.edu/

Abstract: This paper seeks to evaluate the performance of genetic algorithms (GA) as an alternative procedure for generating optimal or near-optimal solutions for location problems. The specific problems considered are the uncapacitated and capacitated fixed charge problems, the maximum covering problem, and competitive location models. We compare the performance of the GA-based heuristics developed against well-known heuristics from the literature, using a test base of publicly available data sets. Scope and purpose Genetic algorithms are a potentially powerful tool for solving large-scale combinatorial optimization problems. This paper explores the use of this category of algorithms for solving a wide class of location problems. The purpose is not to prove that these algorithms are superior to procedures currently utilized to solve location problems, but rather to identify circumstances where such methods can be useful and viable as an alternative/superior heuristic solution method.

Additional Information

Computers and Operations Research, Vol. 29, 761-779 (2002). doi:10.1016/S0305-0548(01)00021-1
Language: English
Date: 2002
Genetic algorithms, combinatorial optimization problems, location problems, heuristic solution methods