隨機旅行時間下車輛補貨路線規劃之研究

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姓名

黃瀚正(Han-jheng Huang) 查詢紙本館藏

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土木工程學系

論文名稱

隨機旅行時間下車輛補貨路線規劃之研究
(Routing for the truck replenishment problem under stochastic travel times)

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摘要(中)

在車輛補貨問題中，如何有效地調派車輛並將貨物準時送達，為一重要課題，過去有關車輛補貨相關研究，很少考量旅行時間之隨機性，大多以平均旅行時間做為排程之依據，在實際營運時若隨機擾動過大，將造成原本排程失去最佳性。而目前實務上車輛補貨排程，大多採規劃人員之經驗進行規劃，此種方式缺乏系統最佳化分析，往往在營運時造成資源的浪費因此，本研究主要於模式中加入旅行時間隨機性考量，建構隨機性車輛補貨路線規劃模式，以提供決策者輔助工具，可有效地規劃車輛補貨路線排程。
本研究利用時空網路流動的技巧，以總運送成本最小化為目標，並考量實際營運下旅行時間之隨機性，建立一隨機性模式，此模式包含車流網路與物流網路。此外，本研究進一步修正隨機性模式之旅行時間唯一固定平均旅行時間，發展一確定性模式。隨機性模式與確定性模式可定式為一含額外限制之整數多重貨物網路流動問題，屬NP-hard問題，當面對實務大規模問題，難以在有限時間內求得最佳解。因此，本研究透過問題分解，並配合數學規劃軟體CPLEX，發展一有效率之啟發式演算法。為評估確定性模式與隨機性模式於實際營運之績效，本研究發展一模擬評估方法，以比較兩模式之優劣。最後，為評估模式與演算法求解效率，本研究以台灣一供應商運送業者為例，進行範例測試，並進行不同參數之敏感度分析，結果顯示隨機模式表現比確定性模式為佳，於實際營運中可大幅地降低未滿足需求之額外處理量產生。

摘要(英)

In truck replenishment problem, how to deliver goods efficiently on time becomes a more important problem. In the past few years, many VRP under deterministic travel time has been discussed and researched by many scholars. But stochastic disturbances arising from variations in vehicle travel times in actual operations are neglected. Then the stochastic travel time will make the planned schedule lose its optimality. Therefore, we constructed a stochastic truck replenishment model that considered the influence of stochastic travel times.
We employed network flow techniques with the objective of minimizing total cost to construct the stochastic model that considered the stochastic travel times, including vehicle-flow and commodity-flow networks. Then, we modified the stochastic travel time in the stochastic truck replenishment model as an average travel to develop a deterministic model. Both stochastic model and deterministic model are formulated as the integer multiple commodity network flow problem, which is characterized as NP-hard. Since the problem sizes are expected to be huge in real practice, the models are difficult to be solved in a reasonable time. Therefore, we develop an effective heuristic algorithm by adopting a problem decomposition technique, coupled with a mathematical programming solver CPLEX. To evaluate how well the stochastic model and the deterministic model, we also developed a simulation-based evaluation method. Finally, we use a real data and suitable assumptions and sensitive analysis to test our model. The test results of stochastic model is better than deterministic model because stochastic model produces low shortage cost.

關鍵字(中)

★ 隨機性旅行時間
★ 多重貨物網路流動問題
★ 時空網路
★ 啟發解
★ 車輛補貨

關鍵字(英)

★ Stochastic travel times
★ Time-space network
★ Multiple commodity network flow problem
★ Heuristics
★ Truck replenishment

論文目次

摘要 i
ABSTRACT ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章緒論 1
1.1研究背景與動機 1
1.2研究目的與範圍 3
1.3研究方法與流程 3
第二章文獻回顧 6
2.1車輛途程問題(Vehicle Routing Problem, VRP) 6
2.1.1車輛途程問題 6
2.1.2具時窗限制車輛途程問題(Vehicle Routing Problem With Time Window, VRPTW) 10
2.2時空網路相關文獻 13
2.3大型含額外限制整數網路流動問題啟發式演算法 16
2.4隨機性問題之相關理論與文獻 19
2.4.1隨機性問題相關理論 19
2.4.2隨機擾動相關文獻 20
2.5小結 22
第三章模式構建 24
3.1問題描述 24
3.2隨機性車輛補貨路線規劃模式 24
3.2.1模式基本假設或已知資訊 24
3.2.2隨機性旅行時間模式之時空網路 26
3.2.2.1車流時空網路 26
3.2.2.2物流時空網路 30
3.2.2.3非預期懲罰成本說明 33
3.2.3模式符號說明 41
3.2.4數學定式 42
3.3確定性日營運車輛配送模式 43
3.3.1確定性模式之時空網路 44
3.3.2確定性模式之數學定式 46
3.4模擬評估方法 47
3.5模式應用 47
3.6小結 48
第四章模式求解 49
4.1演算法初始解 49
4.2演算法改善解 55
4.3範例驗證 57
4.4 小結 58
第五章範例測試 60
5.1輸入資料 60
5.1.1補貨排程規劃所需之相關參數 60
5.1.2車隊資料 60
5.1.3貨物成本資料 61
5.2模式發展 62
5.2.1問題規模 62
5.2.2模式輸入資料 63
5.3電腦演算環境及設定 64
5.3.1電腦演算環境 64
5.3.2相關參數設定 64
5.3.3模式輸出資料 65
5.4測試結果與分析 65
5.4.1隨機性補貨模式測試結果 66
5.4.2不同模式間之分析比較 67
5.5敏感度分析 68
5.5.1車流提早狀況之非預期懲罰成本之敏感度分析 68
5.5.2後續受影響貨物量之敏感度分析 70
5.6情境分析 72
5.6.1車流提早狀況之非預期懲罰成本之折減率分析 72
5.7小結 74
第六章結論與建議 75
6.1結論 75
6.2建議 76
6.3貢獻 76
參考文獻 78
附錄 85
附錄一 CPLEX Callable Library Code 85
附錄二零售點資料 86

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指導教授

顏上堯(Shang-Yao Yan)

審核日期

2011-8-5

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