5.1 結論
1. 本研究基因演算法使用許多隨機的概念，以機率做為演算法則的依據，做廣度的搜 尋，能找出較好的路徑組合且增加其多樣化。
2. 本研究在建構完初始族群後，利用改善路線交錯的方式求得一個較好的母體，在時 間範圍內能搜尋到不錯解。
3. 在敏感度分析上，證實懲罰乘數和初始母體族群的大小會影響求解的品質，且呈現 正相關。
4. 使用基因演算法尋優，之後再利用路徑改善方法，可在合理的時間內找出不錯的路 徑組合。
5. 本研究的改善交錯方法能明顯降低總旅行成本。
6. 在合理的車輛數下，能夠有效降低違反時間窗顧客數。
5.2 建議
1. 可針對基因演算法，加入其他相關的機制，使其能在合理時間內求得最佳解。亦可 結合其他適合 VRPTW 的啟發式解法發展新的演算法，提高求解品質與效率。
2. 本研究假設距離和速度的比例是相同，但此情況和實際狀況不符，演算方法中若能 考量到實際上的車速，在實務上會更有貢獻。
3. 在第二階段改善法上，後續的研究可加入 or-opt、2-opt、2-opt*、1-1 節點交換或 1-0 交換等改善方法。
4. 未來研究可考慮即時路況系統與旅行時間，使物流業者可透過線上系統即時更新路 線，以減少旅行時間成本且可增加效率。
5. 未來研究可考慮多場站，更能符合實務業者的需求。
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簡歷
姓 名：陳綠茵 籍 貫：桃園縣
出生日期：72 年 9 月 17 日
電子郵件：green.tem95g@nctu.edu.tw 學 歷：
國立交通大學運輸科技與管理學研究所 東吳大學經濟學系
德明技術學院