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An Efficient Hybrid Particle Swarm Optimization Algorithm for Solving the Uncapacitated Continuous Location-Allocation Problem

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  • Abdolsalam Ghaderi

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  • Mohammad Jabalameli
  • Farnaz Barzinpour
  • Ragheb Rahmaniani

Abstract

Location-allocation problems are a class of complicated optimization problems that determine the location of facilities and the allocation of customers to the facilities. In this paper, the uncapacitated continuous location-allocation problem is considered, and a particle swarm optimization approach, which has not previously been applied to this problem, is presented. Two algorithms including classical and hybrid particle swarm optimization algorithms are developed. Local optima of the problem are obtained by two local search heuristics that exist in the literature. These algorithms are combined with particle swarm optimization to construct an efficient hybrid approach. Many large-scale problems are used to measure the effectiveness and efficiency of the proposed algorithms. Our results are compared with the best algorithms in the literature. The experimental results show that the hybrid PSO produces good solutions, is more efficient than the classical PSO, and is competitive with the best results from the literature. Additionally, the proposed hybrid PSO found better solutions for some instances than did the best known solutions in the literature. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Abdolsalam Ghaderi & Mohammad Jabalameli & Farnaz Barzinpour & Ragheb Rahmaniani, 2012. "An Efficient Hybrid Particle Swarm Optimization Algorithm for Solving the Uncapacitated Continuous Location-Allocation Problem," Networks and Spatial Economics, Springer, vol. 12(3), pages 421-439, September.
  • Handle: RePEc:kap:netspa:v:12:y:2012:i:3:p:421-439
    DOI: 10.1007/s11067-011-9162-y
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    File URL: http://hdl.handle.net/10.1007/s11067-011-9162-y
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    References listed on IDEAS

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    1. Lozano, S. & Guerrero, F. & Onieva, L. & Larraneta, J., 1998. "Kohonen maps for solving a class of location-allocation problems," European Journal of Operational Research, Elsevier, vol. 108(1), pages 106-117, July.
    2. Melo, M.T. & Nickel, S. & Saldanha-da-Gama, F., 2009. "Facility location and supply chain management - A review," European Journal of Operational Research, Elsevier, vol. 196(2), pages 401-412, July.
    3. Yapicioglu, Haluk & Smith, Alice E. & Dozier, Gerry, 2007. "Solving the semi-desirable facility location problem using bi-objective particle swarm," European Journal of Operational Research, Elsevier, vol. 177(2), pages 733-749, March.
    4. Rosing, K. E., 1992. "An optimal method for solving the (generalized) multi-Weber problem," European Journal of Operational Research, Elsevier, vol. 58(3), pages 414-426, May.
    5. Mauricio Resende & Renato Werneck, 2007. "A fast swap-based local search procedure for location problems," Annals of Operations Research, Springer, vol. 150(1), pages 205-230, March.
    6. Bischoff, M. & Klamroth, K., 2007. "An efficient solution method for Weber problems with barriers based on genetic algorithms," European Journal of Operational Research, Elsevier, vol. 177(1), pages 22-41, February.
    7. ReVelle, C.S. & Eiselt, H.A. & Daskin, M.S., 2008. "A bibliography for some fundamental problem categories in discrete location science," European Journal of Operational Research, Elsevier, vol. 184(3), pages 817-848, February.
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    Cited by:

    1. Seyedmehdi Mirmohammadsadeghi & Shamsuddin Ahmed, 2015. "Memetic Heuristic Approach for Solving Truck and Trailer Routing Problems with Stochastic Demands and Time Windows," Networks and Spatial Economics, Springer, vol. 15(4), pages 1093-1115, December.

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