| Summary: | 碩士 === 朝陽科技大學 === 建築及都市設計研究所 === 92 === This thesis proposes a model for use in selecting the location for a park facility where an uncertainty population is taken as a factor. In other existing models, the uncertainty population factor is simplified by using a fixed value or a value based on existing but inaccurate data. These methods oversimplify the issue and do not reflect actual circumstances, thereby causing inaccurate decisions. The proposed model seeks to rectify this problem. Taiwan has already reached a certain level of development that the old models are no longer appropriate. The proposed model seeks to complement the current situation while taking into consideration the uncertainty population factor.
In the research conducted for the creation of the proposed model, detailed reviews of existing location theories, Genetic Algorithm, Monte Carlo simulation and Simulation Optimization have been made. An application regression model is used to make a projection on the uncertainty population and consideration to the uncertainty distance of travel. The Monte Carlo simulation is used for risk estimation. Integrate P-median concept of location theory to build an urban neighborhood park location selection basic model under uncertainty factors. Finally, the use of Genetic Algorithm in RISKOptimizer effectively shortens the computation time that would generate the optimum results in the selection of a location for the park facility.
A case study has been made using the proposed model in selecting a location for a park facility in the West district of Taichung City in Taiwan. The proposed model considered the uncertain population of the neighborhood in the future and was able to generate results that reflected the actual circumstance more than the existing models.
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