A Two-Level Parallel Genetic Algorithm for the Uncapacitated Warehouse  Location Problem

A new genetic algorithm (GA) for the uncapacitated warehouse location problem (UWLP) and its parallelization are described. The parallel method is based on two ideas. (1) The GA is using a new integer coding for the UWLP. (2) The parallelization takes advantage of a developed two- level strategy. The first level of parallelization consists of executing several subpopulations of the GA concurrently with the occasional migration of individuals between them. On the second level, the solution space is separated into several disjunctive parts. The developed method, which is the first application of a parallel metaheuristic to the UWLP, is evaluated using a large set of 717 benchmark problems available from the literature, whereas the other known solution methods are always applied to subsets of these instances. The results show, that the parallel method is competitive with the best known solution methods so far.

[1]  John E. Beasley,et al.  OR-Library: Distributing Test Problems by Electronic Mail , 1990 .

[2]  Xin Yao,et al.  Fast Evolution Strategies , 1997, Evolutionary Programming.

[3]  Erick Cantú-Paz,et al.  Theory of Parallel Genetic Algorithms , 2005 .

[4]  Teodor Gabriel Crainic,et al.  Parallel Metaheuristics Applications , 2005 .

[5]  Diptesh Ghosh,et al.  Neighborhood search heuristics for the uncapacitated facility location problem , 2003, Eur. J. Oper. Res..

[6]  César Rego,et al.  A simple filter-and-fan approach to the facility location problem , 2006, Comput. Oper. Res..

[7]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[8]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[9]  R. D. Galvão,et al.  A method for solving to optimality uncapacitated location problems , 1990 .

[10]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[11]  Pascal Van Hentenryck,et al.  A simple tabu search for warehouse location , 2004, Eur. J. Oper. Res..

[12]  M. Resende,et al.  Chapter 2 GRASP WITH PATH-RELINKING : RECENT ADVANCES AND APPLICATIONS , 2005 .

[13]  Michael Affenzeller.  A GENERIC EVOLUTIONARY COMPUTATION APPROACH BASED UPON GENETIC ALGORITHMS AND EVOLUTION STRATEGIES , 2003 .

[14]  Celso C. Ribeiro,et al.  GRASP with Path-Relinking: Recent Advances and Applications , 2005 .

[15]  S. Domek,et al.  A generic evolutionary computation approach based upon genetic algorithms and evolution strategies , 2002 .

[16]  Martin Hoefer,et al.  Experimental Comparison of Heuristic and Approximation Algorithms for Uncapacitated Facility Location , 2003, WEA.

[17]  J. Krarup,et al.  Sharp Lower Bounds and Efficient Algorithms for the Simple Plant Location Problem , 1977 .

[18]  Hans-Paul Schwefel,et al.  Evolution strategies – A comprehensive introduction , 2002, Natural Computing.

[19]  Yuri Kochetov,et al.  Computationally Difficult Instances for the Uncapacitated Facility Location Problem , 2005 .

[20]  Erick Cantú-Paz,et al.  Migration Policies, Selection Pressure, and Parallel Evolutionary Algorithms , 2001, J. Heuristics.

[21]  Mehmet Emin Aydin,et al.  Solving large-scale uncapacitated facility location problems with evolutionary simulated annealing , 2006 .

[22]  Fabián A. Chudak,et al.  Near-optimal solutions to large-scale facility location problems , 2005, Discret. Optim..

[23]  Enrique Alba,et al.  Measuring the Performance of Parallel Metaheuristics , 2005 .

[24]  J. Krarup,et al.  The simple plant location problem: Survey and synthesis , 1983 .

[25]  C. Fogarty,et al.  Two Approaches to Simulated Annealing for Uncapacitated Facility Location Problems , 2002 .

[26]  Mauricio G. C. Resende,et al.  A hybrid multistart heuristic for the uncapacitated facility location problem , 2006, Eur. J. Oper. Res..

[27]  Erick Cantú-Paz,et al.  A Survey of Parallel Genetic Algorithms , 2000 .

[28]  Mauricio G. C. Resende,et al.  Greedy Randomized Adaptive Search Procedures , 1995, J. Glob. Optim..

[29]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[30]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[31]  Minghe Sun,et al.  A Tabu Search Heuristic for the Uncapacitated Facility Location Problem , 2005 .

[32]  Rajan Batta,et al.  On the use of genetic algorithms to solve location problems , 2002, Comput. Oper. Res..

[33]  Mauricio G. C. Resende,et al.  A Hybrid Heuristic for the p-Median Problem , 2004, J. Heuristics.

[34]  Celso C. Ribeiro,et al.  Greedy Randomized Adaptive Search Procedures , 2003, Handbook of Metaheuristics.

[35]  Mohammad Abdulrahman Al-Fawzan,et al.  A tabu search approach to the uncapacitated facility location problem , 1999, Ann. Oper. Res..

[36]  Minghe Sun.  Solving the uncapacitated facility location problem using tabu search , 2006, Comput. Oper. Res..

[37]  Jozef Kratica,et al.  Solving the simple plant location problem by genetic algorithm , 2001, RAIRO Oper. Res..

[38]  Pascal Van Hentenryck,et al.  A Multistart Variable Neighborhood Search for Uncapacitated Facility Location , 2005 .

[39]  Teodor Gabriel Crainic,et al.  Parallel Strategies for Meta-Heuristics , 2003, Handbook of Metaheuristics.