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Differential Evolution Algorithms Solving a Multi-Objective, Source and Stage Location-Allocation Problem

  • Thongdee, Thongpoon (Department of Industrial Engineering, Faculty of Engineering Ubon Ratchathani University) ;
  • Pitakaso, Rapeepan (Department of Industrial Engineering, Faculty of Engineering Ubon Ratchathani University)
  • Received : 2014.01.19
  • Accepted : 2015.03.13
  • Published : 2015.03.30

Abstract

The purpose of this research is to develop algorithms using the Differential Evolution Algorithm (DE) to solve a multi-objective, sources and stages location-allocation problem. The development process starts from the design of a standard DE, then modifies the recombination process of the DE in order improve the efficiency of the standard DE. The modified algorithm is called modified DE. The proposed algorithms have been tested with one real case study (large size problem) and 2 randomly selected data sets (small and medium size problems). The computational results show that the modified DE gives better solutions and uses less computational time than the standard DE. The proposed heuristics can find solutions 0 to 3.56% different from the optimal solution in small test instances, while differences are 1.4-3.5% higher than that of the lower bound generated by optimization software in medium and large test instances, while using more than 99% less computational time than the optimization software.

Keywords

Location Allocation Problem;Meta-Heuristics;Differential Evolution;Multi-Objective Optimization;Ethanol Plant

Acknowledgement

Supported by : Energy Conservation Promotion Fund

References

  1. Buddadee, B., Wirojanagud, W., Watts, D. J., and Pitakaso, R. (2008), The development of multi-objective optimization model for excess bagasse utilization: A case study for Thailand, Environmental Impact Assessment Review, 28(6), 380-391. https://doi.org/10.1016/j.eiar.2007.08.005
  2. Buddadee, B., Wirojanagud, W., Techarungpaisan, P. and Pitakaso, R. (2009), Environmental system optimization of excess bagassse utilization for sugar mills in the Northeastern of Thailand, Thai Environmental Engineering Journal, 24(2), 1-13.
  3. Caballero, R. M., Gonzalez, F. M., Guerrero, J. M., and Paralera, C. (2007), Solving a multiobjective location routing problem with a metaheuristic based on tabu search: application to a real case in Andalusia, Eur. J. Oper. Res., 177, 1751-1763. https://doi.org/10.1016/j.ejor.2005.10.017
  4. Doerner, K. F., Gutjahr, W. J., and Nolz, P. C. (2009), Multi-criteria location planning for public facilities in tsunami-prone coastal areas, OR Spectrum, 31(3), 651-678. https://doi.org/10.1007/s00291-008-0126-7
  5. Doerner, K., Focke, A. and Gutjahr, W. J. (2007), Multicriteria tour planning for mobile healthcare facilities in a developing country, Eur. J. Oper. Res., 179, 1078-1096. https://doi.org/10.1016/j.ejor.2005.10.067
  6. Drezner, T., Drezner, Z. and Salhi, S. (2006), A multiobjective heuristic approach for the casualty collection points location problem, J. Oper. Res. Soc., 57, 727-734. https://doi.org/10.1057/palgrave.jors.2602047
  7. Leung, S. C. H. (2007), A non-linear goal programming model and solution method for the multi-objective trip distribution problem in transportation engineering, Optim. Eng., 8, 277-298. https://doi.org/10.1007/s11081-007-9019-x
  8. Lin, C. K. Y. and Kwok, R. C. W. (2006), Multiobjective metaheuristics for a location-routing problem with multiple use of vehicles on real data and simulated data, Eur. J. Oper. Res., 175(3), 1833-1849. https://doi.org/10.1016/j.ejor.2004.10.032
  9. Medaglia, A. L., Villegas, J. G., and Rodriguez-Coca, D. M. (2009), Hybrid bi-objective evolutionary algorithms for the design of a hospital waste management network, J. Heuristics, 15, 153-176. https://doi.org/10.1007/s10732-008-9070-6
  10. Nanthasamroeng, N., Pitakaso, R., and Buddadee, B. (2008), A multi objectives model for multi-echelon location problem: Application in ethanol plant location analysis in Thailand, Proceedings of the International Conference on Intelligent Manufacturing and Logistics, Waseda University.
  11. Thongdee, T. and Pitakaso, R. (2012), Solving a multiobjective, source and stage location-allocation problem: a case study of a bagasse and cassava pulp ethanol plant in northeastern Thailand, KKU Res. J., 17(1), 71-87.
  12. Thongdee, T. and Pitakaso, R. (2013), Solving a Multi-Objective, Source and Stage Location-Allocation Problem Using DE-PSO, Proceedings of the Operations Research Network Conference, Ubon Ratchathani University, 584-589.
  13. Uno, T. and Katagiri, H. (2008), Single- and multiobjective defensive location problems on a network, Eur. J. Oper. Res., 188, 76-84. https://doi.org/10.1016/j.ejor.2007.04.003
  14. Xu, J., Liu, Q., and Wang, R. (2008), A class of multiobjective supply chain networks optimal model under random fuzzy environment and its application to the industry of Chinese liquor, Inform. Sci., 178, 2022-2043. https://doi.org/10.1016/j.ins.2007.11.025

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