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A Problem-Specific and Effective Metaheuristic for Flexibility Design

  • Jörn Grahl

    ()

    (Johannes Gutenberg-Universität Mainz, Dept. of Information Systems & Business Administration Mainz School of Management and Economics)

  • Michael Schneider

    (Technische Universität Kaiserslautern, Information Systems and Operations Research Department)

  • David Francas

    (Universität Mannheim, Chair of Logistics and Supply Chain Management)

Registered author(s):

    Matching uncertain demand with capacities is notoriously hard. Operations managers can use mix-flexible resources to shift excess demands to unused capacities. To find the optimal configuration of a mix-flexible production network, a flexibility design problem (FDP) is solved. Existing literature on FDPs provides qualitative structural insights, but work on solution methods is rare. We contribute the first metaheuristic which integrates these structural insights and is specifically tailored to solve FDPs. Our genetic algorithm is compared to commercial solvers on instances of up to 15 demand types, resources, and 500 demand scenarios. Experimental evidence shows that in the realistic case of flexible optimal configurations, it dominates the comparison methods regarding runtime and solution quality.

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    File URL: http://www.macro.economics.uni-mainz.de/RePEc/pdf/Discussion_Paper_1001.pdf
    File Function: First version, 2010
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    Paper provided by Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz in its series Working Papers with number 1001.

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    Length: 24 pages
    Date of creation: 28 Jan 2010
    Date of revision: 28 Jan 2010
    Handle: RePEc:jgu:wpaper:1001
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    1. Kauder, S. & Meyr, H., 2009. "Strategic network planning for an international automotive manufacturer," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 36058, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Charles H. Fine & Robert M. Freund, 1990. "Optimal Investment in Product-Flexible Manufacturing Capacity," Management Science, INFORMS, vol. 36(4), pages 449-466, April.
    3. Wallace J. Hopp & Eylem Tekin & Mark P. Van Oyen, 2004. "Benefits of Skill Chaining in Serial Production Lines with Cross-Trained Workers," Management Science, INFORMS, vol. 50(1), pages 83-98, January.
    4. 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.
    5. William C. Jordan & Stephen C. Graves, 1995. "Principles on the Benefits of Manufacturing Process Flexibility," Management Science, INFORMS, vol. 41(4), pages 577-594, April.
    6. Seyed M. Iravani & Mark P. Van Oyen & Katharine T. Sims, 2005. "Structural Flexibility: A New Perspective on the Design of Manufacturing and Service Operations," Management Science, INFORMS, vol. 51(2), pages 151-166, February.
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