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Extensions to STaTS for practical applications of the facility layout problem

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  • Scholz, Daniel
  • Jaehn, Florian
  • Junker, Andreas

Abstract

We consider a very general case of the facility layout problem, which allows incorporating various aspects appearing in real life applications. These aspects include loose requirements on facilities' footprints, each of which only needs to be of rectangular shape and can optionally be restricted concerning the surface area or the aspect ratio. Compared to former approaches other generalizations of practical relevance are multiple, not necessarily rectangular workshops, exclusion zones in workshops, predefined positions of facilities, the consideration of aisles, and the adherence of further restrictions such as the enforced placement of certain facilities next to an exterior wall or a minimum distance between certain pairs of facilities. Although different objectives could be applied, we especially focus on the most relevant one in practice, the minimization of transportation costs. We show that this problem can heuristically be solved using an extension of the Slicing Tree and Tabu Search (STaTS) based approach. The application of this algorithm on practical data shows its effectiveness. The paper concludes with a step-by-step guide for the application of STaTS in practice.

Suggested Citation

  • Scholz, Daniel & Jaehn, Florian & Junker, Andreas, 2010. "Extensions to STaTS for practical applications of the facility layout problem," European Journal of Operational Research, Elsevier, vol. 204(3), pages 463-472, August.
    • Handle: RePEc:eee:ejores:v:204:y:2010:i:3:p:463-472
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    References listed on IDEAS

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    1. Scholz, Daniel & Petrick, Anita & Domschke, Wolfgang, 2009. "STaTS: A Slicing Tree and Tabu Search based heuristic for the unequal area facility layout problem," European Journal of Operational Research, Elsevier, vol. 197(1), pages 166-178, August.
    2. Goetschalckx, Marc, 1992. "An interactive layout heuristic based on hexagonal adjacency graphs," European Journal of Operational Research, Elsevier, vol. 63(2), pages 304-321, December.
    3. Hanif D. Sherali & Barbara M. P. Fraticelli & Russell D. Meller, 2003. "Enhanced Model Formulations for Optimal Facility Layout," Operations Research, INFORMS, vol. 51(4), pages 629-644, August.
    4. Scholz, Daniel & Petrick, Anita & Domschke, Wolfgang, 2009. "STaTS: A Slicing Tree and Tabu Search based heuristic for the unequal area facility layout problem," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 39430, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    5. Kar Yan Tam, 1992. "Genetic algorithms, function optimization, and facility layout design," European Journal of Operational Research, Elsevier, vol. 63(2), pages 322-346, December.
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    Cited by:

    1. I. Jerin Leno & S. Saravana Sankar & S. G. Ponnambalam, 2018. "MIP model and elitist strategy hybrid GA–SA algorithm for layout design," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 369-387, February.
    2. Emde, Simon & Boysen, Nils, 2012. "Optimally locating in-house logistics areas to facilitate JIT-supply of mixed-model assembly lines," International Journal of Production Economics, Elsevier, vol. 135(1), pages 393-402.

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