考量充電下移動式智能櫃運送排程最佳化之研究

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唐顥瑋(Hao-Wei Tang) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

考量充電下移動式智能櫃運送排程最佳化之研究

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摘要(中)

在目前市場經濟條件下，物流配送佔據整體供應鏈的相當大比例，且物流配送的支出持續增加。因此，物流已成為影響生產成本和流通成本的關鍵因素之一。若能針對配送環節進行改善，將有助於減少成本損耗，並為物流領域帶來正面的效益。有幾種方法可以改進物流配送，以降低成本並提高效率。首先，路線規劃和運輸計劃是一個重要的方向。透過使用現代的路線規劃技術和智能運輸系統，可以確保貨物的最佳運送路徑，減少不必要的里程和時間浪費。其次，引進節能環保技術也是值得考慮的一個方向。例如使用環保型車輛和節能設備，不僅可以降低燃料消耗和碳的排放，還能節約運輸成本。若臺灣物流業者能提前引進移動式智能櫃進行收送貨物的運送排程，不僅可以實現節能減碳目標，減少燃油和人力等相關成本，亦可以提高貨物運送效率，且能減少一般貨車與物流士運送所造成的道路安全相關問題。
本研究模式為考量充電下移動式智能櫃運送排程最佳化，於實務上考量相關條件限制，以營運成本最小化為目標，構建一考量充電下移動式智能櫃運送排程模式，再配合CPLEX數學規劃軟體進行模式求解。因模式問題規模過大，求解時間過長且不合理，為增加求解效率，故本研究撰寫C++程式語言發展啟發式演算法進行求解測試。為了評估此模式與演算法之實用與可靠性，以桃園市某物流公司之資料為測試範例，發展四種啟發式演算法進行測試結果比較，並選擇較優的啟發式演算法進行不同參數之敏感度分析。研究結果顯示，本研究提出之模式與啟發式演算法於實務上有較好的成效，可提供決策單位作為考量充電下移動式智能櫃運送排程最佳化之參考。

摘要(英)

In the current market economy conditions, logistics distribution occupies a significant proportion of the overall supply chain, and the expenses related to logistics distribution are continually increasing. As a result, logistics has become a key factor influencing production and distribution costs. Improving the distribution process can help reduce cost wastage and bring positive benefits to the logistics sector. There are several methods to enhance logistics distribution for cost reduction and increased efficiency. Firstly, optimizing route planning and transportation strategies is crucial. Utilizing modern route planning techniques and intelligent transportation systems can ensure the most efficient delivery routes, reducing unnecessary mileage and time wastage. Secondly, adopting energy-saving and environmentally friendly technologies is worth considering. For example, using eco-friendly vehicles and energy-efficient equipment not only reduces fuel consumption and carbon emissions but also saves transportation costs. If Taiwanese logistics operators could introduce mobile smart containers for shipping scheduling, it would not only achieve energy-saving and carbon reduction goals but also reduce related costs such as fuel and labor. Additionally, it could enhance cargo delivery efficiency and mitigate road safety issues caused by conventional trucks and logistics personnel transportation.

This study focuses on the optimization of charging-based mobile smart container shipping scheduling, considering relevant practical constraints, with the objective of minimizing operational costs. A model for the charging-based mobile smart container shipping scheduling is constructed and then solved using the CPLEX mathematical optimization software. However, due to the large scale of the model, the solution time becomes excessively long and impractical. To increase the solution efficiency, this research develops a heuristic algorithm in C++ programming language for testing. To assess the practicality and reliability of this model and algorithm, data from a logistics company in Taoyuan City is used as a testing example. Four heuristic algorithms are developed and compared for testing results, and the optimal heuristic algorithm is selected for sensitivity analysis with different parameters. The research results demonstrate that the proposed model and heuristic algorithm have good performance in practical applications, providing decision-makers with references for optimizing charging-based mobile smart container shipping scheduling.

關鍵字(中)

★ 移動式智能櫃
★ 收送貨路徑規劃問題
★ 時空網路
★ 啟發式解法

關鍵字(英)

★ Mobile parcel locker
★ Pick-up and delivery problem
★ Time-space network
★ Heuristic

論文目次

摘要 i
ABSTRACT ii
謝誌 iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章緒論 1
1.1 研究背景與動機 1
1.2 研究範圍與目的 3
1.3 研究方法與流程 4
第二章文獻回顧 5
2.1 商用無人車智取櫃規劃之相關文獻 5
2.2 時空網路應用之相關文獻 8
2.3 大型含額外限制整數網路流動問題啟發式演算法之相關文獻 9
2.4 文獻評析 12
第三章模式建構 13
3.1 問題描述 13
3.2 模式架構 13
3.2.1 模式基本假設 14
3.2.2 移動式智能櫃收送貨之時空網路 15
3.2.2.1 移動式智能櫃的車流網路 16
3.2.2.2 移動式智能櫃的送貨與收貨時空網路 20
3.2.3 符號說明與數學定式 24
3.2.3.1 符號說明 24
3.3.2.2 數學定式 26
3.3 模式驗證 30
3.4小結 34
第四章啟發式演算法設計 35
4.1 啟發式演算法求解 35
4.1.1 時間優先法 35
4.1.2 距離優先法 39
4.1.3 最小比率法 42
4.1.4 門檻值法 45
4.2 啟發式演算法應用 50
4.3 小結 51
第五章範例測試 52
5.1 資料輸入 52
5.1.1 移動式智能櫃規劃資料 52
5.1.2 運輸路網規劃資料 56
5.1.3 收、送貨物供需資料 58
5.2 模式發展 60
5.2.1 問題規模 60
5.2.2 電腦演算環境 61
5.2.3 模式輸入資料 61
5.2.4 模式輸出資料 61
5.3 範例測試結果分析 62
5.3.1 時間優先法測試結果 62
5.3.2 距離優先法測試結果 64
5.3.3 最小比率法測試結果 66
5.3.4 門檻值法測試結果 68
5.3.5 測試結果比較 71
5.3.6 演算法績效分析 73
5.4 敏感度分析 74
5.4.1 移動式智能櫃移動成本之敏感度分析 77
5.4.2 移動式智能櫃充電成本之敏感度分析 81
5.4.3 移動式智能櫃承載限制之敏感度分析 86
5.4.4 收送貨物需求規模之敏感度分析 90
5.4.5 移動式智能櫃耗電量之敏感度分析 95
5.4.6 移動式智能櫃電量上下限之敏感度分析 99
5.5 管理意涵 107
5.6 小結 109
第六章結論與建議 110
6.1 結論 110
6.2 建議 111
6.3 貢獻 112
參考文獻 114
附錄 119

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指導教授

顏上堯(Shang-Yao Yan)

審核日期

2023-8-11

推文