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A statistical analysis of simulated annealing applied to the p-median problem

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  • Fernando Chiyoshi
  • Roberto Galvão

Abstract

We present a statistical analysis of simulated annealing applied to the p-median problem. The algorithm we use combines elements of the vertex substitution method of Teitz and Bart with the general methodology of simulated annealing. The cooling schedule adopted incorporates the notion of temperature adjustments rather than just temperature reductions. Computational results are given for test problems ranging from 100 to 900 vertices, retrieved from Beasley's OR-Library for combinatorial problems. Each problem was run for a maximum of 100 different streams of random numbers. Optimal solutions were found for 26 of the 40 problems tested, although high optimum hitting rates were obtained for only 20 of them. The worst gap in relation to the optimal solution was 1.62%, after all runs for each of the test problems were computed. Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • Fernando Chiyoshi & Roberto Galvão, 2000. "A statistical analysis of simulated annealing applied to the p-median problem," Annals of Operations Research, Springer, vol. 96(1), pages 61-74, November.
    • Handle: RePEc:spr:annopr:v:96:y:2000:i:1:p:61-74:10.1023/a:1018982914742
    DOI: 10.1023/A:1018982914742
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    Citations

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    Cited by:

    1. Joshua Q. Hale & Enlu Zhou & Jiming Peng, 2017. "A Lagrangian search method for the P-median problem," Journal of Global Optimization, Springer, vol. 69(1), pages 137-156, September.
    2. Michael Brusco & Hans-Friedrich Köhn, 2009. "Exemplar-Based Clustering via Simulated Annealing," Psychometrika, Springer;The Psychometric Society, vol. 74(3), pages 457-475, September.
    3. Kenneth Carling & Xiangli Meng, 2016. "On statistical bounds of heuristic solutions to location problems," Journal of Combinatorial Optimization, Springer, vol. 31(4), pages 1518-1549, May.
    4. Jianing Zhi & Burcu B. Keskin, 2018. "A Multi-Product Production/Distribution System Design Problem with Direct Shipments and Lateral Transshipments," Networks and Spatial Economics, Springer, vol. 18(4), pages 937-972, December.
    5. Antiopi Panteli & Basilis Boutsinas & Ioannis Giannikos, 2021. "On solving the multiple p-median problem based on biclustering," Operational Research, Springer, vol. 21(1), pages 775-799, March.
    6. B. Jayalakshmi & Alok Singh, 2017. "A hybrid artificial bee colony algorithm for the p-median problem with positive/negative weights," OPSEARCH, Springer;Operational Research Society of India, vol. 54(1), pages 67-93, March.
    7. Oded Berman & Zvi Drezner & Arie Tamir & George Wesolowsky, 2009. "Optimal location with equitable loads," Annals of Operations Research, Springer, vol. 167(1), pages 307-325, March.
    8. Rajagopalan, Hari K. & Vergara, F. Elizabeth & Saydam, Cem & Xiao, Jing, 2007. "Developing effective meta-heuristics for a probabilistic location model via experimental design," European Journal of Operational Research, Elsevier, vol. 177(1), pages 83-101, February.
    9. Mladenovic, Nenad & Brimberg, Jack & Hansen, Pierre & Moreno-Perez, Jose A., 2007. "The p-median problem: A survey of metaheuristic approaches," European Journal of Operational Research, Elsevier, vol. 179(3), pages 927-939, June.
    10. Lim, Seow & Kuby, Michael, 2010. "Heuristic algorithms for siting alternative-fuel stations using the Flow-Refueling Location Model," European Journal of Operational Research, Elsevier, vol. 204(1), pages 51-61, July.
    11. Brusco, Michael J., 2014. "A comparison of simulated annealing algorithms for variable selection in principal component analysis and discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 38-53.
    12. Carling, Kenneth & Han, Mengjie & Håkansson, Johan & Meng, Xiangli & Rudholm, Niklas, 2014. "Measuring CO2 Emissions Induced by Online and Brick-and-mortar Retailing," HUI Working Papers 106, HUI Research.

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