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Automatic generation of efficient policy alternatives via simulation-optimization

Author

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  • J S Yeomans

    (Management Science Area, Schulich School of Business, York University)

Abstract

Simulation-optimization methods can be used for many practical and industrial problems in which some or all of the system components are stochastic. These techniques can be applied to a wide variety of problem types, including those in which some functions cannot be represented analytically. In contrast to earlier function optimization approaches, in this paper, these techniques are used for generating several new policy options for planning applications. By using this approach, multiple policy alternatives can be created that meet established system criteria, while simultaneously remaining acceptable and implementable in practice. A subsequent comparative evaluation of the alternatives would be undertaken prior to final policy selection. An illustrative application of the method is provided to demonstrate the usefulness of this approach in the planning phase of policy design.

Suggested Citation

  • J S Yeomans, 2002. "Automatic generation of efficient policy alternatives via simulation-optimization," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(11), pages 1256-1267, November.
    • Handle: RePEc:pal:jorsoc:v:53:y:2002:i:11:d:10.1057_palgrave.jors.2601439
    DOI: 10.1057/palgrave.jors.2601439
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    Citations

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    Cited by:

    1. Julian Yeomans, 2011. "Efficient generation of alternative perspectives in public environmental policy formulation: applying co-evolutionary simulation–optimization to municipal solid waste management," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 19(4), pages 391-413, December.
    2. A K Miranda & E Del Castillo, 2011. "Robust parameter design optimization of simulation experiments using stochastic perturbation methods," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(1), pages 198-205, January.

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