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Application of Advanced Hybrid Genetic Algorithms for Optimal Locations of High School

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Computational Science and Its Applications – ICCSA 2013 (ICCSA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7974))

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Abstract

In this study, an advanced hybrid genetic algorithms is formulated and applied to the optimal location of high schools in a rural area of Bangladesh. The advanced hybrid algorithms consist of genetic algorithm and alternating location allocation algorithm. The model is applied to Nakhla Upazila (a sub-district area) of Bangladesh as a case study. First, the genetic algorithm is used to generate optimum locations of high schools and then coded with the traditional alternating location allocation heuristic thus avoiding very long computation time. Obtained simulation results indicate that existing high schools are not well distributed and they are far from residential areas. The developed hybrid algorithm based model successfully shows the best locations of high schools in the Nakhla Upazilla minimizing the total amount of travel distance from the different zones of Nakhla Upazila to the school sites and thus provide a safe travel for children. The results have an implication for a good urban planning in context of placing schools optimally within walking distance of a neighborhood The model thus developed can be applied by planners as a useful tool for any location analysis.

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References

  1. Neema, M., Maniruzzaman, K., Ohgai, A.: New genetic algorithms based approaches to continuous p-median problem. Networks and Spatial Economics (published online), doi:10.1007/s11067-008-9084-5

    Google Scholar 

  2. Cooper, L.: Location-allocation problem. Oper. Res. 11, 331–343 (1963)

    Article  MATH  Google Scholar 

  3. Michalewicz, Z.: Facility location on a plane model, BASIC Microcomputer Programs for Urban Analysis and Planning. Chapman and Hall, New York (1985)

    Google Scholar 

  4. Narula, S.C., Ogbu, U.I.: An hierarchal location-allocation problem. Omega 7, 137–143 (1979)

    Article  Google Scholar 

  5. Neebe, A.: A branch and bound algorithm for the p-median transportation problem. Journal of the Operational Research Society 29, 989–995 (1978)

    MATH  Google Scholar 

  6. Goldengorin, B., Ghosh, D., Sierksma, G.: Branch and peg algorithms for the simple plant location problem. Comput. Oper. Res. 31, 241–255 (1963)

    Article  MathSciNet  Google Scholar 

  7. Okabe, A., Boots, B., Sugihara, K.: Spatial Tessellations: Concepts and Applications of Voronoi Diagrams. John Wiley & Sons Ltd., Chichester (1992)

    MATH  Google Scholar 

  8. Lorena, L., Senne, E.: A column generation approach to capacitated p-median problems, http://www.lac.inpe.br/~lorena/senne/col-gen-cpmp-final.pdf

  9. Senne, E., Lorena, L.: A lagrangean/surrogate approach to p-median problems, http://www.lac.inpe.br/~lorena/pmed99.pdf

  10. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Co., Inc., Redwood City (1989)

    MATH  Google Scholar 

  11. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Co., Inc., Redwood City (1989)

    MATH  Google Scholar 

  12. Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  13. Michalewicz, Z.: An introduction to genetic algorithms. The MIT Press, Cambridge (1996)

    Google Scholar 

  14. Krzanowski, R., Raper, J.: Spatial Evolutionary Modeling. Oxford University Press, New York (2001)

    Google Scholar 

  15. Michalewicz, Z.: Genetic Algorithms: A Survey. IEEE Computer (1994)

    Google Scholar 

  16. Houck, C., Joines, J., Kay, M.: Comparison of genetic algorithms, random restart and two-opt switching for solving large location-allocation problems. Comput. Oper. Res. 23(6), 587–596 (1999)

    Article  Google Scholar 

  17. Guerra, G., Lewis, J.: Spatial optimization and gis., http://www.esri.com/news/arcuser/0402/files/optimize.pdf

  18. Krzanowski, R., Raper, J.: Hybrid genetic algorithm for transmitter location in wireless networks. Comput. Environ. Urban. Syst. 23, 359–382 (1999)

    Article  Google Scholar 

  19. Love, R.F., Juel, H.: Properties and solution methods for large location-allocation problems. Journal of the Operational Research Society 33, 443–452 (1982)

    MATH  Google Scholar 

  20. Feng, C.M., Lin, J.: Using a genetic algorithm to generate alternative sketch maps for urban planning, Computers. Environment and Urban Systems 23, 91–108 (1999)

    Article  Google Scholar 

  21. Levine, D.: Application of a hybrid genetic algorithm to airline crew scheduling. Computers and Operation Research 23(6), 547–558 (1996)

    Article  MATH  Google Scholar 

  22. Kelly, J.D., Davis, L.: Hybridizing the genetic algorithm and the k nearest neighbors classification algorithm. In: Proceedings of the 4th International Conference on Genetic Algorithms, pp. 370–383 (1991)

    Google Scholar 

  23. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 2nd extended edn. Springer, New York (1994)

    Google Scholar 

  24. Gong, D., Gen, M., Xu, W., Yamazaki, G.: Hybrid evolutionary method for obstacle location-allocation. Comput. Ind. Eng. 29(1-4), 525–530 (1995)

    Article  Google Scholar 

  25. B.B.B.: of Statistics, Bangladesh Population Census 2001: Sherpur Zila, BBS, Dhaka (2001)

    Google Scholar 

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Neema, M.N., Maniruzzaman, K.M., Ohgai, A. (2013). Application of Advanced Hybrid Genetic Algorithms for Optimal Locations of High School. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2013. ICCSA 2013. Lecture Notes in Computer Science, vol 7974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39649-6_21

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  • DOI: https://doi.org/10.1007/978-3-642-39649-6_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39648-9

  • Online ISBN: 978-3-642-39649-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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