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研究生： 陳英顏
Chen, Ying-Yen
論文名稱： 特定時窗下配送與撿收問題之研究
A Study on Delivery and Pickup Problems with Time Windows
指導教授： 王小璠
Wang, Hsiao-Fan
口試委員: 溫于平
顏上堯
姚銘忠
張國華
學位類別： 博士
Doctor
系所名稱： 工學院 - 工業工程與工程管理學系
Department of Industrial Engineering and Engineering Management
論文出版年： 2012
畢業學年度： 100
語文別： 英文
論文頁數： 104
中文關鍵詞： 車輛途程 、時窗 、配送與撿收 、確定與不確定狀況 、機會限制型規劃 、可信賴度 、共演化演算法
外文關鍵詞： Vehicle routing, Time windows, Delivery and pickup, Certain and uncertain environment, Chance constrained programming, Credibility measure, Coevolutionary algorithm
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    • 由於環境保育意識的抬頭，具有環保意識的綠色產業愈來愈顯得重要，這些綠色產業包括有回收、拆解、再利用及再製造等產業。相對的，逆物流在這當中也扮演了重要角色。新近，將正向物流系統及逆向物流系統整合成一個雙向物流系統已經愈來愈普遍了，也因此特定時窗下配送與撿收問題逐漸受到了關注與重視。
    針對特定時窗下配送與撿收問題本研究檢視了三個現行主要的物流策略及其衍生而出的物流架構。其中，「特定時窗下同時配送與撿收問題」缺乏適切的規劃模式與問題集，本研究特別針對其特性探討。本研究更進一步提出兩個更一般性的架構：「特定時窗下彈性配送與撿收問題」及「特定時窗下模糊彈性配送與撿收問題」。
    針對上述的三種問題，本研究提出了對應的數學規劃模式並以Cplex軟體驗證之，同時產生了適切的問題集以利各種問題之進一步分析。在觀察了問題的特性後，本研究發展了一套共演化演算法來求解這些問題集。經過了大量的運算試驗後，發現共演化演算法在準確度及效率上皆有相當優異的表現。
    另外，本研究針對四個確定性的架構進行了比較性的探討，發現「特定時窗下彈性配送與撿收問題」架構最彈性、最有效率而且也最經濟。至於不確定性的架構，本研究藉由運用一個以可信賴度為基礎之機會限制型規劃模式，發現不同類型的決策者可以選用不同的信心水準進而找出其最適切之解決方案。

    Reverse logistics plays an important role in environmental conscious manufacturing, including recycling, disassembly, reuse, and remanufacturing. Integrating a forward logistic system and a reverse logistic system to form a bi-directional logistic system hence becomes significant. Subsequently, the Delivery and Pickup Problems with Time Windows (DPPTWs) has drawn much attention.
    Three current DPPTW schemes derived from the three main logistic strategies were reviewed in this study. Among them, the Simultaneous Delivery and Pickup Problem with Time Windows (SDPPTW) lacked for an appropriate model and appropriated test problems; thus, it was investigated first in this study. Then, two more general schemes: the Flexible Delivery Pickup Problem with Time Windows (FDPPTW) and the Fuzzy Flexible Delivery Pickup Problem with Time Windows (FFDPPTW) were thoroughly studied.
    For these problems, the corresponding mathematical models were proposed and validated by Cplex. Since they are NP-hard, the solution procedures entitled Coevolutionary Algorithms (CEAs) were developed for solving the generated test problems. The computational results reveal the excellent effectiveness and efficiency of the developed CEAs. Moreover, a comparative study on crisp schemes shows that the FDPPTW is the most flexible, efficient, and economical scheme for certain environment. For the uncertain case, the FFDPPTW based on a credibility measure was proposed in the form of the Chance Constrained Programming Model to facilitate the decision support based on the decision maker’s preference.

    ABSTRACT i 中 文 摘 要 iii TABLE CAPTIONS ix FIGURE CAPTIONS xi LIST OF NOTATIONS xiii Chapter 1 INTRODUCTION 1 1.1 Background of the Delivery and Pickup Problems 2 1.2 Motivation and Purposes 3 1.3 Framework of the Dissertation 6 Chapter 2 LITERATURE REVIEW 9 2.1 Delivery and Pickup Problems 9 2.2 Delivery and Pickup Problems with Time Windows 11 2.3 Fuzzy Vehicle Routing Problems 13 2.4 Fuzzy Credibility Theory 15 2.5 Evolutionary Algorithms 18 2.6 Summary and Conclusion 21 Chapter 3 ISSUES AND THE PROPOSED MODELS 25 3.1 Fuzzy Flexible Delivery and Pickup Problem with Time Windows (FFDPPTW) 25 3.1.1 Problem Description of the FFDPPTW 25 3.1.2 The Proposed Model of the FFDPPTW 28 3.1.3 Summary and Discussion of the FFDPPTW 33 3.2 Special Case 1: Flexible Delivery and Pickup Problem with Time Windows (FDPPTW) 34 3.2.1 Problem Description of the FDPPTW 35 3.2.2 The Proposed Model of the FDPPTW 36 3.2.3 Summary and Discussion of the FDPPTW 37 3.3 Special Case 2: Simultaneous Delivery and Pickup Problem with Time Windows (SDPPTW) 38 3.3.1 Problem Description of the SDPPTW 38 3.3.2 The Proposed Model of the SDPPTW 40 3.3.3 Summary and Discussion of the SDPPTW 42 3.4 Summary and Discussion 43 Chapter 4 THE PROPOSED SOLUTION PROCEDURE – COEVOLUTIONARY ALGORITHM 45 4.1 Framework of the Coevolutionary Algorithm 45 4.2 Encoding 47 4.3 Initial Population 47 4.3.1 Cheapest Insertion Method 48 4.3.2 Multi-parameter Cheapest Insertion Method 49 4.3.3 Random Seeds Cheapest Insertion Method 50 4.3.4 Crossover 50 4.3.5 Construction of the Initial Population 51 4.4 Co-evolution 52 4.4.1 Reproducing 53 4.4.2 Recombination 54 4.4.3 Local Improvement 55 4.4.4 Crossover 56 4.4.5 Mutation 57 4.4.6 Selection 60 4.5 Termination Condition 61 4.6 Implementation and Analysis 62 4.6.1 Parameters for the SDPPTW 62 4.6.2 Factorial Experiments for the FDPPTW 64 4.6.3 Framework of the Algorithm for the FDPPTW and the FFDPPTW 65 4.7 Conclusion and Discussion 68 Chapter 5 NUMERICAL EXAMPLES AND EVALUATION 69 5.1 SDPPTW Test Problems 69 5.1.1 Evaluation of the Accuracy and Efficiency of CEA 70 5.1.2 Comparison between GA and CEA 71 5.2 FDPPTW Test Problems 74 5.2.1 Evaluation of the Accuracy and Efficiency of CEA 74 5.2.2 Comparison between SDPPTW and FDPPTW 75 5.3 FFDPPTW Test Problems 77 5.3.1 Evaluation of the Accuracy and Efficiency of CEA 77 5.3.2 Results of the Credibility Approach 80 5.4 Comparative Studies and Analysis 82 5.5 Conclusion and Discussion 88 Chapter 6 SUMMARY AND CONCLUSION 91 6.1 Summary of the Dissertation 92 6.2 Conclusion 93 6.3 Possible Future Work 94 REFERENCES 97 APPENDIX A 103

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