大量傷病患事故之救護車派遣問題，相關決策因素複雜，加上同時需考量保留一定數量之救護車以因應例行日常救護事故，因此在分秒必爭的狀況下，單憑救護派遣員的經驗及人為的判斷，難以在有限的時間內，做出最佳的救護車派遣決策。有鑑於此，本研究旨在構建大量傷病患事故之救護車派遣問題的數學模式，以決定最佳之出勤的救護車、傷患後送順序及後送醫院，並同時考量例行日常救護案件，以在合理的時間內計算最佳之派遣方式，使大量傷病患及例行救護病患的整體死亡人數最小化。
本研究利用死亡率與時間之函數關係，估計大量傷病患及日常救護病患之死亡人數，以一般收送貨問題結合最大存活選址問題，構建救護車派遣之數學模式。因本問題屬為NP-hard問題，故本研究建構一以回溯適應性門檻接受法為基礎之啟發式解法，結合數學規劃軟體，發展一有效之求解演算法。
最後，本研究建構32種測試例，並與最廣為被使用之後送決策方法進行比較，證實本研究建構之數學模式，可有效率的降低總死亡人數。

The determinants of the ambulance dispatching for mass casualty incidents (MCIs) in consideration of routine daily emergencies are so complex that it is difficult to make the optimal decision efficiently solely depending on dispatchers' experience and human judgment. The objective of the study is to propose an ambulance dispatching model for MCIs to minimize the overall death including both a MCI and routine daily emergencies.
We used the death rate, a function of time, to estimate the death toll of the MCI and daily emergencies, and developed an ambulance dispatching model based on the general pickup and delivery problem and the maximal survival location problem. The model is an NP-hard problem, and thus we proposed a backtracking adaptive threshold accepting based heuristic with the assistance of a mathematical programming solver.
When tested with 32 instances and compared results with the most widely-used triage method, START, the proposed heuristic can efficiently obtain the solutions that decreased the overall death toll.

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