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Simulated annealing and tabu search approaches for the Corridor Allocation Problem

Author

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  • Ahonen, H.
  • de Alvarenga, A.G.
  • Amaral, A.R.S.

Abstract

In the Corridor Allocation Problem, we are given n facilities to be arranged along a corridor. The arrangements on either side of the corridor should start from a common point on the left end of the corridor. In addition, no space is allowed between two adjacent facilities. The problem is motivated by applications such as the arrangement of rooms in office buildings, hospitals, shopping centers or schools. Tabu search and simulated annealing algorithms are presented to minimize the sum of weighted distances between every pair of facilities. The algorithms are evaluated on several instances of different sizes either randomly generated or available in the literature. Both algorithms reached the optimal (when available) or best-known solutions of the instances with n⩽30. For larger instances with size 42⩽n⩽70, the simulated annealing implementation obtained smaller objective values, while requiring a smaller number of function evaluations.

Suggested Citation

  • Ahonen, H. & de Alvarenga, A.G. & Amaral, A.R.S., 2014. "Simulated annealing and tabu search approaches for the Corridor Allocation Problem," European Journal of Operational Research, Elsevier, vol. 232(1), pages 221-233.
    • Handle: RePEc:eee:ejores:v:232:y:2014:i:1:p:221-233
    DOI: 10.1016/j.ejor.2013.07.010
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    References listed on IDEAS

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    Cited by:

    1. Gintaras Palubeckis, 2020. "An Approach Integrating Simulated Annealing and Variable Neighborhood Search for the Bidirectional Loop Layout Problem," Mathematics, MDPI, vol. 9(1), pages 1-30, December.
    2. Junqi Liu & Zeqiang Zhang & Feng Chen & Silu Liu & Lixia Zhu, 2022. "A novel hybrid immune clonal selection algorithm for the constrained corridor allocation problem," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 953-972, April.
    3. Anjos, Miguel F. & Vieira, Manuel V.C., 2017. "Mathematical optimization approaches for facility layout problems: The state-of-the-art and future research directions," European Journal of Operational Research, Elsevier, vol. 261(1), pages 1-16.
    4. Dahlbeck, Mirko & Fischer, Anja & Fischer, Frank, 2020. "Decorous combinatorial lower bounds for row layout problems," European Journal of Operational Research, Elsevier, vol. 286(3), pages 929-944.
    5. A. R. S. Amaral, 2022. "A heuristic approach for the double row layout problem," Annals of Operations Research, Springer, vol. 316(2), pages 1-36, September.
    6. Dahlbeck, Mirko & Fischer, Anja & Fischer, Frank & Hungerländer, Philipp & Maier, Kerstin, 2023. "Exact approaches for the combined cell layout problem," European Journal of Operational Research, Elsevier, vol. 305(2), pages 530-546.
    7. Gintaras Palubeckis & Armantas Ostreika & Jūratė Platužienė, 2022. "A Variable Neighborhood Search Approach for the Dynamic Single Row Facility Layout Problem," Mathematics, MDPI, vol. 10(13), pages 1-27, June.
    8. Herrán, Alberto & Manuel Colmenar, J. & Duarte, Abraham, 2021. "An efficient variable neighborhood search for the Space-Free Multi-Row Facility Layout problem," European Journal of Operational Research, Elsevier, vol. 295(3), pages 893-907.
    9. Jinlong Yuan & Lei Wang & Xu Zhang & Enmin Feng & Hongchao Yin & Zhilong Xiu, 2015. "Parameter identification for a nonlinear enzyme-catalytic dynamic system with time-delays," Journal of Global Optimization, Springer, vol. 62(4), pages 791-810, August.
    10. Palubeckis, Gintaras, 2015. "Fast simulated annealing for single-row equidistant facility layout," Applied Mathematics and Computation, Elsevier, vol. 263(C), pages 287-301.
    11. Xing Wan & Xing-Quan Zuo & Xin-Chao Zhao, 2021. "A Surrogate Model-Based Hybrid Approach for Stochastic Robust Double Row Layout Problem," Mathematics, MDPI, vol. 9(15), pages 1-18, July.

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