本文主要乃針對有關區位選擇的問題中，人口預測的不確定性因素對於公園 設施區位選擇的影響做一探討及研究。過去在求解設施最佳區位問題時，常常把 問題單純化或是將區位選擇問題中所隱藏的不確定性因素以事先定義好的值 (predefined value) 來設定之，如此一來問題則變得單純且容易解決矣。然而其缺 點卻是無法有效反映現實狀況，並使得設施區位設定的決策產生相當之誤差。臺 灣都市目前已朝向多元化發展，傳統的區位選擇方式已無法解決含有具不確定性 因素的區位問題，因此，實有必要發展出一套可以針對區位選擇中具有不確定性 因素進行風險評估的區位選擇模式，以提供最新且符合本文所探討的不確定性因 素下公園設施最佳區位選擇之模式。 本研究將針對相關區位理論、基因演算法 (Genetic Algorithms, GA)、蒙地卡 羅模擬法 (Monte Carlo Simulation)、及模擬最佳化 (Simulation Optimization) 法 進行研究方法的探討，同時並將應用迴歸模式 (regression model) 以推估在區位 選擇問題中的未來人口數，以及考慮旅行距離中的不確定性因素，並以蒙地卡羅 模擬法進行其預測上之風險評估。本研究並結合區位理論中P-中位數法的概念， 建構出一個在不確定性因素下公園設施最佳區位選擇的基本模式。在本研究中， 採用RISK Optimizer 最佳化軟體以進行模擬最佳化法並可有效的縮短搜尋可行 解的時間，並進一步求解出公園設施的最佳區位之選擇。 本研究最後以臺中市西區之鄰里公園區位選擇來進行實證研究，並依據各鄰 里的未來人口推估數量以求解出公園設施興建之最佳區位位置。由於此種求解模 式和步驟考量區位選擇中具有不確定性的影響因素，因此其所求解出來的結果也 將更能符合現實環境需求的情況。

This paper 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. A regression model is used to make a projection on the uncertainty population and to consider the uncertainty distance of travel. The Monte Carlo simulation is used for risk estimation. This study then integrates P-median concept of location theory to build a basic model on the selection of an urban neighborhood park location 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 better than the existing models