近年來受電子商務發展、網路購物盛行等，物流業越來越受到重視，不論政府或企業都相當關心物流業的發展，而運輸又是其中非常重要的一部分，根據2018年統計資料，美國運輸成本所占總物流成本高達63%，如此高的比例壓縮企業獲利的空間，因此，若在運輸指派時採用適當的需求預測及預規劃路線，可以增加利潤並使公司整體效率提高，讓物流業發展更加進步。
過去在學術上探討運輸路線問題多半是在確定性條件下進行，然而這樣的假設僅能在學術上使用，在實務運輸配送時，這些變數是不確定性的，因此在路線規劃時需採取不同方法來處理變數。此為一隨機車輛途程問題(Stochastic Vehicle Routing Problem)，本研究著重探討隨機需求位置問題，並以統計分析驗證所提分佈之可信度，過去在探討相關的隨機環境問題時所假設的隨機性多缺乏實際資料而使用假設數據，本研究則根據實際數據產生之分佈型態來做後續問題之探討。
本研究以個案物流公司歷史資料為分析素材，並以台北市為研究範圍，利用Arena模擬系統預測需求點產生情形，並利用Matlab求解最佳化路線規劃問題，目標為提供一符合歷史配送資料隨機性的訂單模型及輸配送管理方案。

In recent years, logistics industry becomes popular due to the prevailing on E-commerce and online shopping. Since transportation is one of the most important operations in this industry, the reductions of transportation costs (i.e. up to 63% of the total logistics cost in USA) is the main challenge issue of existing logistics service providers.
Most of previous studies on transportation routing problem were mainly conducted with given and known information. However, the nature of stochastic demand has rarely been captured. This study focus on research of stochastic demand point, and aim to provide a reliable distribution based on statistical analysis. In this study, we initially fit the distributions of demand points based on historic and reality data and continues the following research based on the distribution pattern.
By using Arena to simulate the potential demand points based on the historical data analysis, and solving the routing problem with heuristic algorithms, the goal of this paper aims to establish a reliable model to provide an model that can generate order based on historical data and improve delivery performance.

第一章緒論 1
1.1研究背景與動機 1
1.1.1美國物流業成本概況 1
1.1.2台灣汽車貨運業概況 3
1.2研究目的 4
1.3研究範圍 4
1.4研究流程 6
第二章 文獻回顧 7
2.1車輛途程問題 7
2.1.1車輛途程問題之類型 7
2.1.2隨機車輛途程問題(SVRP) 11
2.2城市物流 13
2.3分群數量的決定方法 16
2.4系統模擬 18
2.5小結 19
第三章研究方法 21
3.1研究問題 21
3.2問題定義與求解步驟 22
3.2.1問題定義 22
3.2.2求解步驟 22
3.3模型建構 23
3.4求解步驟完整流程圖 29
第四章案例分析 31
4.1小案例測試 31
4.1.1案例一 32
4.1.2案例二 33
4.2大型案例分析 38
4.3小結 50
第五章結論 51
5.1結論與管理意涵 51
5.2未來研究建議 52
參考文獻 53
附錄 58

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