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ABSTRACT

An evolutionary algorithm for some instances of the single-source capacitated plant location problem encodes candidate solutions in two permutations, one of plant locations and a second of customers, with an integer that indicates the number of open locations. A greedy decoder identifies the solution such a genotype represents, and the EA searches for good solutions using only selection and mutation. In tests on 36 problem instances, the EA is competitive with a recent algorithm, though two superficially promising heuristic extensions do not improve its performance. The results support the general effectiveness of permutation codings in EAs that search for optimum subsets.

REFERENCES

• 1: Maria João Cortinhal , Maria Eugénia Captivo, Genetic algorithms for the single source capacitated location problem, Metaheuristics: computer decision-making, Kluwer Academic Publishers, Norwell, MA, 2004
• 2: J. E. Beasley. Obtaining test problems via Internet. Journal of Global Optimization, 8:429--433, 1996.

INDEX TERMS

Primary Classification:
  G. Mathematics of Computing
  G.2 DISCRETE MATHEMATICS
      G.2.1 Combinatorics

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search

General Terms:
Algorithms

Keywords:
capacitated, evolutionary algorithm, facility location, permutation coding, plant location, single-source, warehouse location