在整個機場的營運作業中，機門指派在機場的營運效率與服務水準上扮演關鍵性的角色。然而機場實際營運中，時常發生班機隨機到離延誤的情形，當延誤導致班機使用機門時間有衝突時，目前國內則以人工方式進行重新指派動作，此人工經驗為主的指派方式，在班次密集的尖峰時段將不能確定其指派效率與績效。因此若能在規劃時期考量實際營運時班機隨機延誤的特性，提供規劃人員較符合實際擾動之指派結果，使其在實際營運中不受到大幅擾動，增加在實際營運的適用性，將是一個相當重要的課題。本研究將以機場營運者為立場，考量實際營運時班機隨機到離延誤之特性，發展出整合靜態規劃與即時階段之求解架構，其中包含以完全資訊為概念發展完全資訊模式，進一步以完全資訊模式為概念，考量即時階段班機指派時間順序發展隨機性模式。期望能幫助機場相關人員有效地考量班機隨機延誤擾動下的規劃指派結果，進而提升機門指派作業之整體績效。 本研究除了發展兩整合模式，並進一步探討理論上整合問題之最佳性。此外，本研究在完全資訊模式以數學規劃軟體求解中小型問題，但兩模式之大型問題規模龐大，因此以分解式演算法的概念針對兩模式特性發展有效的求解演算法，且對完全資訊模式以劃分問題的方式發展下限解演算法，並相同以劃分問題方式發展上限解演算法，配合數學規劃軟體進行求解。其次，隨機性模式為一非線性零壹整數規劃問題，因此同樣以分解式演算法為基礎，以隨機事件為考量發展分解式演算法，再配合數學規劃軟體進行求解。最後，本研究以台灣桃園國際機場為例，進行範例測試與分析，結果甚佳，顯示本研究所建構知模式與求解方法應可為未來機場當局之參考。 The assignment of flights to gates is essential to an airport，s operation and level of service. However, stochastic flight delays often occur in real world. The aircraft may not be assigned to the planned gate due to the stochastic flight delays in real operations. As a result, an airport staff has to reassign flights to gates in real-time operations and the actual performance of the original assignments could be reduced. Therefore, considering of stochastic flight delays is necessary in actual operations. It also helps airport authority to handle the assignment of flights to gates and improve the performance of gate assignment problem. A integrated framework which contain the gates planning and the real-time stages, is introduced to solve the stochastic programming problem. We develop the perfect information model and the gate reassignment model base on a integrated framework. The two models solve the stochastic flight delay. First, the perfect information model is established based on the concept of perfect information. Second, the stochastic model considers of prioritize time of the flight assignment. It is expected to that this is a useful tool for authorities to effectively handle gates reassignment and thus enhance current airport operation. We also study optimality of integrated problems except for developing two models. Besides, the two models could not solve large-scale problems optimally by using mathematics programming software. In order to solve large-scale problems efficiently, we develop decomposition algorithms each for these two models. The stochastic model is nonlinear mathematics programming program. The preliminary results are good, showing that the models could be useful for a Taiwan international airport.
