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| Author: | Huang, G.H. |
| Title: | Policy planning under uncertainty: efficient starting populations for simulation-optimization methods applied to municipal solid waste management |
| Journal: | Journal of Environmental Management
2005 : OCT, VOL. 77:1, p. 22-34 |
| Index terms: | methodology policy making uncertainty waste |
| Language: | eng |
| Abstract: | This article combines evolutionary simulation-optimization (ESO) with grey programming (GP) for policy planning to create a hybrid solutionapproach named GESO. It is shown that multiple policy alternatives meeting required system criteria, or modelling-to-generate-alternatives (MGA), can be quickly and efficiently created by applying GESO to this case data. The efficacy of GESO is illustrated using a municipal solid waste management case taken from regional municipality in the Province of Ontario, Canada. The MGA capability of GESO is especially meaningful for large-scale real-world planning porblems and the practicality of this procedure can easily be extended from MSW systems to many other planning applications containing significant sources of uncertainty. |
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