Uber公司經營模式屬於差異化服務模式，差異化服務能夠滿足不同顧客的需求以獲得更便利之服務，如此讓顧客自主選擇，當顧客需求不一定的時候，業者便能快速獲得顧客信賴及較高平均利潤，而Uber定價方式則是為經驗法則或是約定俗成而制定，在社會轉型的狀況下，其約定俗成之定價模式未必是最好的模式。本研究以Uber為例，探討搜尋台北車站之差異化服務之價格最佳解，為了提高預約交易成功率藉此降低資源閒置，進而獲取更高獲利。本研究實驗環境架構以粒子群演算法進行最佳組合解搜尋，利用模擬的建置去產生平均利潤，來達到減少人力與資材損耗以及做好時間上的控管。結果顯示，使用粒子群演算法之差異化服務之比原Uber公司之差異化服務價格，可以得到更好的平均利潤，在服務至上的時代中，透過差異化服務模式經營公司，利用粒子群演算法搜尋適合的價格，是可以對企業有更高的競爭力與永續發展的機會。

The operational mode of Uber is differentiated service mode and the differentiated service can meet the needs of different customers to provide the more convenient services, so that customers can make a choice independently. When customer’s demands are uncertain, the operators can gain the customer’s reliance and higher profits in a rapid way. Uber’s pricing method is established according to the experience principle or popular use. Under the circumstance of social transformation, the conventional pricing model is not necessarily the best. With Uber as an example, this study explored and searched for the optimum solution to the price of differentiated service in Taipei Station to increase the success rate of reservation transaction, so as to reduce the idle resources and further gain the higher profits. In terms of the experimental framework of this study, particle swarm optimization was applied to search for the optimal combination solution and the simulated establishment was used to produce average profit to reduce the human resource and wastage of materials as well as control and manage the time well. According to the results, compared with the company’s price of the original differentiated service, the differentiated service where the particle swarm optimization is used can gain the higher average profit. In the service-oriented era, it is a better opportunity for the enterprise to gain the higher competitiveness and sustainable development if differentiated service mode and the differentiated service to using particle swarm optimization

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