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An ant colony algorithm for solving budget constrained and unconstrained dynamic facility layout problems

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  • Baykasoglu, Adil
  • Dereli, Turkay
  • Sabuncu, Ibrahim

Abstract

The main characteristic of today's manufacturing environments is volatility. Under a volatile environment, demand is not stable. It changes from one production period to another. To operate efficiently under such environments, the facilities must be adaptive to changing production requirements. From a layout point of view, this situation requires the solution of the dynamic layout problem (DLP). DLP is a computationally complex combinatorial optimization problem for which optimal solutions can only be found for small size problems. It is known that classical optimization procedures are not adequate for this problem. Therefore, several heuristics including taboo search, simulated annealing and genetic algorithm are applied to this problem to find a good solution. This work makes use of the ant colony optimization (ACO) algorithm to solve the DLP by considering the budget constraints. The paper makes the first attempt to show how the ACO can be applied to DLP with the budget constraints. In the paper, example applications are presented and computational experiments are performed to present suitability of the ACO to solve the DLP problems. Promising results are obtained from the solution of several test problems.

Suggested Citation

  • Baykasoglu, Adil & Dereli, Turkay & Sabuncu, Ibrahim, 2006. "An ant colony algorithm for solving budget constrained and unconstrained dynamic facility layout problems," Omega, Elsevier, vol. 34(4), pages 385-396, August.
    • Handle: RePEc:eee:jomega:v:34:y:2006:i:4:p:385-396
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    References listed on IDEAS

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    1. Bharat K. Kaku & Joseph B. Mazzola, 1997. "A Tabu-Search Heuristic for the Dynamic Plant Layout Problem," INFORMS Journal on Computing, INFORMS, vol. 9(4), pages 374-384, November.
    2. Balakrishnan, Jaydeep & Jacobs, F. Robert & Venkataramanan, Munirpallam A., 1992. "Solutions for the constrained dynamic facility layout problem," European Journal of Operational Research, Elsevier, vol. 57(2), pages 280-286, March.
    3. E Erel & J B Ghosh & J T Simon, 2003. "New heuristic for the dynamic layout problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(12), pages 1275-1282, December.
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    Cited by:

    1. Yu-Hsin Chen, Gary, 2013. "A new data structure of solution representation in hybrid ant colony optimization for large dynamic facility layout problems," International Journal of Production Economics, Elsevier, vol. 142(2), pages 362-371.
    2. Balakrishnan, Jaydeep & Hung Cheng, Chun, 2009. "The dynamic plant layout problem: Incorporating rolling horizons and forecast uncertainty," Omega, Elsevier, vol. 37(1), pages 165-177, February.
    3. Sachuer Bao & Chi Zhang & Min Ouyang & Lixin Miao, 2019. "An integrated tri-level model for enhancing the resilience of facilities against intentional attacks," Annals of Operations Research, Springer, vol. 283(1), pages 87-117, December.
    4. Vitayasak, Srisatja & Pongcharoen, Pupong & Hicks, Chris, 2017. "A tool for solving stochastic dynamic facility layout problems with stochastic demand using either a Genetic Algorithm or modified Backtracking Search Algorithm," International Journal of Production Economics, Elsevier, vol. 190(C), pages 146-157.
    5. Wu, Desheng & Olson, David L. & Wang, Shouyang, 2019. "Finance-operations interface mechanism and models," Omega, Elsevier, vol. 88(C), pages 1-3.
    6. 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.
    7. Marta S.R. Monteiro & Dalila B.M.M. Fontes & Fernando A.C.C. Fontes, 2012. "Ant Colony Optimization: a literature survey," FEP Working Papers 474, Universidade do Porto, Faculdade de Economia do Porto.

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