本研究探討軌道運輸業團體旅客的訂位系統決策模式，軌道運輸的列車座位容量有限，團體旅客由於訂位需求提早出現、訂位人數具規模、團體票價優惠等特性與單人旅客不同，不可將相同旅次區間的單人旅客與團體旅客視為同質，因此，將團體旅客與單人旅客接受模式分開計算。
考量團體旅客需求隨機特性，結合蒙地卡羅模擬法模擬隨機團體需求，並運用基因演算法求解軌道系統O-D團體訂位數量限制，利用各O-D團體訂位數量限制保護高價值的團體旅客能被接受，此訂位數量可提供營運人員在售票時參考，取代過去以人工經驗判斷是否接受團體的模式。
本研究修改目前臺灣高鐵接受團體旅客需求的流程，加入各O-D訂位數量限制之考量，透過隨機需求模擬旅客訂位情況以驗證本研究模式，並比較兩者之期望收益，本研究結果發現，在O-D訂位數量的保護下，模式接受的團體組合不同，並且一般列車較高鐵模式增加了2%的團體收益。

According to the different characteristics of group passenger in railway industry, this study would like to investigate the passenger reservation system to improve the revenue of group passenger. We formulate a model to determine whether accept or reject the requisition of group passenger and replace the judgment of human-experienced. The paper proposes a method for solving stochastic group demand seat inventory control problems using a hybrid of a genetic algorithm in uncertain environments and the Monte Carlo simulation method. Computation results are analyzed by applying the model to a real-world Taiwan railway system. Analytical results demonstrate that a proper adjustment of the reservation system and accurate booking limit for each O-D pairs improves the revenue of group passenger by 2% for each train.

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