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A swarm intelligence based sample average approximation algorithm for the capacitated reliable facility location problem

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  • Aydin, Nezir
  • Murat, Alper

Abstract

We present a novel hybrid method, swarm intelligence based sample average approximation (SIBSAA), for solving the capacitated reliable facility location problem (CRFLP). The CRFLP extends the well-known capacitated fixed-cost facility problem by accounting for the unreliability of facilities. The standard SAA procedure, while effectively used in many applications, can lead to poor solution quality if the selected sample sizes are not sufficiently large. With larger sample sizes, however, the SAA method is not practical due to the significant computational effort required. The proposed SIBSAA method addresses this limitation by using smaller samples and repetitively applying the SAA method while injecting social learning in the solution process inspired by the swarm intelligence of particle swarm optimization. We report on experimental study results showing that the SIBSAA improves the computational efficiency significantly while attaining same or better solution quality than the SAA method.

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  • Aydin, Nezir & Murat, Alper, 2013. "A swarm intelligence based sample average approximation algorithm for the capacitated reliable facility location problem," International Journal of Production Economics, Elsevier, vol. 145(1), pages 173-183.
    • Handle: RePEc:eee:proeco:v:145:y:2013:i:1:p:173-183
    DOI: 10.1016/j.ijpe.2012.10.019
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    1. R. T. Rockafellar & Roger J.-B. Wets, 1991. "Scenarios and Policy Aggregation in Optimization Under Uncertainty," Mathematics of Operations Research, INFORMS, vol. 16(1), pages 119-147, February.
    2. Arostegui, Marvin Jr. & Kadipasaoglu, Sukran N. & Khumawala, Basheer M., 2006. "An empirical comparison of Tabu Search, Simulated Annealing, and Genetic Algorithms for facilities location problems," International Journal of Production Economics, Elsevier, vol. 103(2), pages 742-754, October.
    3. Schütz, Peter & Tomasgard, Asgeir & Ahmed, Shabbir, 2009. "Supply chain design under uncertainty using sample average approximation and dual decomposition," European Journal of Operational Research, Elsevier, vol. 199(2), pages 409-419, December.
    4. Jeff Linderoth & Alexander Shapiro & Stephen Wright, 2006. "The empirical behavior of sampling methods for stochastic programming," Annals of Operations Research, Springer, vol. 142(1), pages 215-241, February.
    5. Wang, K.-J. & Wang, S.-M. & Chen, J.-C., 2008. "A resource portfolio planning model using sampling-based stochastic programming and genetic algorithm," European Journal of Operational Research, Elsevier, vol. 184(1), pages 327-340, January.
    6. Peng, Peng & Snyder, Lawrence V. & Lim, Andrew & Liu, Zuli, 2011. "Reliable logistics networks design with facility disruptions," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1190-1211, September.
    7. Santoso, Tjendera & Ahmed, Shabbir & Goetschalckx, Marc & Shapiro, Alexander, 2005. "A stochastic programming approach for supply chain network design under uncertainty," European Journal of Operational Research, Elsevier, vol. 167(1), pages 96-115, November.
    8. Chouinard, Marc & D'Amours, Sophie & Aït-Kadi, Daoud, 2008. "A stochastic programming approach for designing supply loops," International Journal of Production Economics, Elsevier, vol. 113(2), pages 657-677, June.
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    Cited by:

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    5. Albareda-Sambola, Maria & Hinojosa, Yolanda & Puerto, Justo, 2015. "The reliable p-median problem with at-facility service," European Journal of Operational Research, Elsevier, vol. 245(3), pages 656-666.
    6. Ayvaz, Berk & Bolat, Bersam & Aydın, Nezir, 2015. "Stochastic reverse logistics network design for waste of electrical and electronic equipment," Resources, Conservation & Recycling, Elsevier, vol. 104(PB), pages 391-404.
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    8. Zamani, Shokufeh & Arkat, Jamal & Niaki, Seyed Taghi Akhavan, 2022. "Service interruption and customer withdrawal in the congested facility location problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    9. Albareda-Sambola, Maria & Landete, Mercedes & Monge, Juan F. & Sainz-Pardo, José L., 2017. "Introducing capacities in the location of unreliable facilities," European Journal of Operational Research, Elsevier, vol. 259(1), pages 175-188.
    10. Huizhen Zhang & Cesar Beltran-Royo & Bo Wang & Ziying Zhang, 2019. "Two-phase semi-Lagrangian relaxation for solving the uncapacitated distribution centers location problem for B2C E-commerce," Computational Optimization and Applications, Springer, vol. 72(3), pages 827-848, April.
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