建議

有關未來研究的方向，建議如下：

(1). 本研究首次提出結合動態等待與動態分區的做法，然而仍有進一步延伸與改 善之空間，可供後續研究參考。

(2). 對於需求環境的模擬設定，未來研究可增加測試時間與空間分佈不均勻的情 況，時間上可有尖離峰不同的時間特性與空間分佈考慮市區與郊區不同需求 的分佈情況，以更符合的實際的應用。

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附錄 A 動態車輛路線問題相關文獻整理

The A Priori Dynamic Traveling Salesman Problem

withTimeWindow Larsen, Madsen, and

Solomon Transportation Science,

2004

Waiting Strategies for Anticipating Service Requests from Known

Customer Locations Thomas Transportation Science,

2007

半 無 DVRP Greedy Algorithm

Longest Wait(LW).

LW Without Stochastic(LW No).

Closest(CL).

Waiting Strategies for the Dynamic Pickup and Delivery Problem with Time

Windows

Mitrovic-Minic and Laporet Transportation Research

PartB, 2004

全 硬 Courier

Dynamic Wait.

Advance Dynamic Wait.

模擬產生 旅行成本 ADW 策略表現最

好。

問題特性 方法架構

Scenario-Based Planning for Partially Dynamic Vehicle

Routing With Stochastic Customers Bent and Hentenryck Operations Research, 2004

半 軟 VRPTW Greedy Algorithm

MSA 修改Solomon 例題

訂單滿足 MSA 效果優於 MPA

Waiting Strategies for Dynamic Vehicle Routing

Brank, Middendorf, Noeth, Dessouky

Transportation Science, 2005

半 無 DVRP 等待策略

Dynamic vehicle routing for Online B2C delivery

Du, Li, and Chou Omega, 2006

半 軟 VRPTW

問題特性 方法架構

Double-horizon Based Heuristics for the Dynamic

Pickup and Delivery Problem with Time

Windows Mitrovic-Minic, Krishnamurti, Laporte Transportation Research

PartB, 2004

全 硬 Courier PDPTW

Cheapest Insertion.

Double-Horizon.

Rolling time

Horizon. 實際資料 旅行成本

採用

Double-Horizon 比 Rolling Horizon.可

附錄 B Voronoi 圖

在平面上任一組點中，有一區域對某一點 a 的距離都比其他點 ai 距離還短，

則此一區域稱為點a 的 Voronoi 多邊形。若將平面上的這些點集合所形成的 Voronoi 多邊形聯集在一起，就會形成 Voronoi 圖。見下圖。

Voronoi 圖

‹ 附錄 C 模擬結果與差異百分比

表C.5 顧客等待時間標準差模擬結果與差異百分比(與不分區比較)

表C.9 勞役分配模擬結果與差異百分比(與不分區比較)

3 完成所有服務時間模擬結果與差異百分比 區比較