本研究之目的在於建構都市公共醫療設施的區位規劃模型,試圖改正過去忽略設施階層性以及規劃條件不明確的問題,以輔助都市規劃者進行醫療設施區位選址的規劃決策工作。 本研究首先了解過去的研究進程,進行公共醫療設施與社會納入、以及醫療設施區位與區位規劃模型等兩個主題相關文獻的回顧,藉由文獻整理與評析,歸納出公共醫療設施區位必須考量的重要因素,包括社會納入、社會公平以及經營績效等;並且整理出公共醫療設施區位規劃模型可以發展的方向,以作為本研究模型構想的基礎。 繼而在模型的決策情境、以及所要處理的問題等構想下,建構本研究的模型。所建構的模型為一個灰色模糊非線性多目標規劃模型,該模型處理了公立與私立設施的區位競爭關係、醫療設施的容量問題、社會納入與社會公平、設施財務、醫療設施的服務層級、以及規劃過程中的不確定性因素等。模型的求解流程包括了五個步驟:資料蒐集與整理、候選節點之篩選、集合之定義、模型求解與整理非劣解集合等。其中,模型求解的步驟裡,對於灰色參數與決策變數的處理,將灰色模型分解成兩個白色子模型,限制決策變數的上界大於或等於下界,然後合併求解;對於模糊關係的處理,採用Verdegay法,以 截集來表達決策者的不明確態度;對於多目標的處理,採用 限制式法;並應用套裝軟體lingo 11.0來輔助求解。 最後應用所建模型,以台北市為空間範圍進行實例分析,來檢視模型的實用性與價值。實例分析結果研擬出三個規劃方案,此三個方案均界定決策者認為低階醫療服務與高階醫療服務同等重要,但分別為強調弱勢族群之照顧、兼顧弱勢族群之照顧及就醫可及性之公平、以及強調就醫可及性之公平。若與配置現況進行比較,三個規劃方案在高階醫療的服務上,不論是對於弱勢族群(高齡者)的服務數量上,或是對於公共醫療的服務效率上,均有較好的改善幅度。 藉由本研究所建構的模型,能夠協助都市規劃者考量公共衛生與社會福利需要,在公共醫療設施區位規劃上有系統化的思考方式與規劃方法。實務上,能運用在新市區開發與舊市區通盤檢討等時機,透過量化資料與系統性的分析,使公共醫療設施區位能夠更為客觀化與系統化。
This study aims to design a location-allocation model for urban public health care facilities. The past studies about location of health care facilities ignored two issues: health care is hierarchical services and uncertain elements exist in location planning works. Thus this study tried to deal with the two issues and develop a useful tool to assist urban planners on location planning of public health care facilities. The study began at literature review on the topics of public health care facility, social inclusion and location-allocation modeling. Based on the literature review, three important considerations for public health care facilities distribution planning including social inclusion, social justice and operational efficiency were identified for model development. Then, a grey, fuzzy, nonlinear and multi-objective programming model considering competitive facilities, service capacity, social inclusion and social justice, finance feasibility, hierarchical services and uncertain elements was developed. The developed model can be solved by the following five steps: collecting parameter data, determining optional locations, identifying covering sets, model solving and identifying non-dominated solutions. In the model solving step, the grey programming approach is used for grey parameters and variables, the Verdegay method is employed for fuzzy constraints, the ?-constraint method is adopted for multiple objectives and the Lingo 11.0 package is applied for problem solving. Finally, the model was applied to Taipei, Taiwan for examining the applicability and usefulness of the model. The case study created three alternatives, which give same importance to low level and high level services. The created alternatives have different emphases: taking care of vulnerable population (social inclusion), fairness of accessing facilities for all population (social justice), and compromise between social inclusion and social justice. Comparing the created alternatives with the present situation, the study indicates that the created alternatives have better performances on high level service than the present situation has. Urban planners can apply the developed model to public health care facility distribution planning in underdeveloped and developed areas considering the needs of public health and social welfare. By using of mathematical programming, location planning of public health care facilities can be objectively and systematically deployed.