請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91428完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蔣明晃 | zh_TW |
| dc.contributor.advisor | Ming-Huang Chiang | en |
| dc.contributor.author | 簡睿閎 | zh_TW |
| dc.contributor.author | JUI-HUNG CHIEN | en |
| dc.date.accessioned | 2024-01-26T16:27:44Z | - |
| dc.date.available | 2024-01-27 | - |
| dc.date.copyright | 2024-01-26 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2024-01-02 | - |
| dc.identifier.citation | [1] 一条菜鸟鱼, 2021. MOEA/D 算法详解[EB/OL]. https://blog.csdn.net/qq_40491534/article/details/121021220.
[2] 洪星宇, 2022. 考量時間及容量限制下之 B2C 電子商務配送模型研究[M]. 國立臺灣大學. [3] 南木长, 2022. 进化计算（七）——MOEA/D 算法详解[EB/OL]. https://blog.csdn.net/qq_43472569/article/details/121234168. [4] 施朝翔, 2015. 基於總成本考量下 B2C 電子商務配送模式之研究[M]. 國立臺灣大學. [5] 張易晟, 2014. B2C 電子商務下都會區配送模型之研究[M]. 國立臺灣大學. [6] ARIYANI A K, MAHMUDY W F, ANGGODO Y P, 2018. Hybrid genetic algorithms and simulated annealing for multi-trip vehicle routing problem with time windows.[J]. International Journal of Electrical & Computer Engineering (2088-8708),8(6). [7] BRAEKERS K, RAMAEKERS K, VAN NIEUWENHUYSE I, 2016. The vehicle routing problem: State of the art classification and review[J]. Computers & industrial engineering, 99: 300-313. [8] CORMEN T H, LEISERSON C E, RIVEST R L, et al., 2022. Introduction to algorithms[M]. MIT press. [9] COSTA L, CONTARDO C, DESAULNIERS G, 2019. Exact branch-price-and-cut algorithms for vehicle routing[J]. Transportation Science, 53(4): 946-985. [10] CRAMER-FLOOD E, 2023. Global retail ecommerce forecast 2023[EB/OL]. https://on.emarketer.com/rs/867-SLG-901/images/eMarketer%20Global%20Retail%20Ecommerce%20Forecast.pdf. [11] DANTZIG G B, RAMSER J H, 1959. The truck dispatching problem[J]. Manage ment science, 6(1): 80-91. [12] DEB K, PRATAP A, AGARWAL S, et al., 2002. A fast and elitist multiobjective genetic algorithm: Nsga-ii[J]. IEEE transactions on evolutionary computation, 6(2):182-197. [13] DI SOMMA M, 2016. Optimal operation planning of distributed energy systems through multi-objective approach: A new sustainability-oriented pathway[Z]. [14] FENG L, ONG Y S, TAN A H, et al., 2015. Memes as building blocks: a case study on evolutionary optimization+ transfer learning for routing problems[J]. Memetic Computing, 7: 159-180. [15] FENG L, ZHOU L, GUPTA A, et al., 2019. Solving generalized vehicle routing problem with occasional drivers via evolutionary multitasking[J]. IEEE transactions on cybernetics, 51(6): 3171-3184. [16] GARCIA-NAJERA A, BULLINARIA J A, 2009. Bi-objective optimization for the vehicle routing problem with time windows: Using route similarity to enhance performance[C]//Evolutionary Multi-Criterion Optimization: 5th International Confer ence, EMO 2009, Nantes, France, April 7-10, 2009. Proceedings 5. Springer: 275-289. [17] GARCIA-NAJERA A, BULLINARIA J A, 2011. An improved multi-objective evo lutionary algorithm for the vehicle routing problem with time windows[J]. Comput ers & Operations Research, 38(1): 287-300. [18] GUTIERREZ-RODRÍGUEZ A E, CONANT-PABLOS S E, ORTIZ-BAYLISS J C,et al., 2019. Selecting meta-heuristics for solving vehicle routing problems with time windows via meta-learning[J]. Expert Systems with Applications, 118: 470-481. [19] HAIMES Y, 1971. On a bicriterion formulation of the problems of integrated system identification and system optimization[J]. IEEE transactions on systems, man, and cybernetics(3): 296-297. [20] HE Y, SUN J, SONG P, et al., 2022. Variable-fidelity hypervolume-based expected improvement criteria for multi-objective efficient global optimization of expensive functions[J]. Engineering with Computers, 38(4): 3663-3689. [21] ISHIBUCHI H, MURATA T, 1998. A multi-objective genetic local search algorithm and its application to flowshop scheduling[J]. IEEE transactions on systems, man, and cybernetics, part C (applications and reviews), 28(3): 392-403. [22] KODURU P, DONG Z, DAS S, et al., 2008. A multiobjective evolutionary-simplex hybrid approach for the optimization of differential equation models of gene net works[J]. IEEE Transactions on Evolutionary Computation, 12(5): 572-590. [23] KUO R J, ZULVIA F E, SURYADI K, 2012. Hybrid particle swarm optimization with genetic algorithm for solving capacitated vehicle routing problem with fuzzy demand–a case study on garbage collection system[J]. Applied Mathematics and Computation, 219(5): 2574-2588. [24] LIN S, KERNIGHAN B W, 1973. An effective heuristic algorithm for the traveling salesman problem[J]. Operations research, 21(2): 498-516. [25] LIU F, LU C, GUI L, et al., 2023. Heuristics for vehicle routing problem: A survey and recent advances[A]. [26] PARETO V, 1919. Manuale di economia politica: con una introduzione alla scienza sociale: volume 13[M]. Società editrice libraria. [27] QI Y, HOU Z, LI H, et al., 2015. A decomposition based memetic algorithm for multi-objective vehicle routing problem with time windows[J]. Computers & Oper ations Research, 62: 61-77. [28] SRINIVAS N, DEB K, 1994. Muiltiobjective optimization using nondominated sort ing in genetic algorithms[J]. Evolutionary computation, 2(3): 221-248. [29] TAN K C, CHEW Y H, LEE L H, 2006. A hybrid multiobjective evolutionary algorithm for solving vehicle routing problem with time windows[J]. computational optimization and applications, 34: 115-151. [30] VIDAL T, 2022. Hybrid genetic search for the cvrp: Open-source implementation and swap* neighborhood[J]. Computers & Operations Research, 140: 105643. [31] WANG Y, CAI Z, GUO G, et al., 2007. Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems[J]. IEEE Trans actions on Systems, Man, and Cybernetics, Part B (Cybernetics), 37(3): 560-575. [32] WU Y, SONG W, CAO Z, et al., 2021. Learning improvement heuristics for solving routing problems[J]. IEEE transactions on neural networks and learning systems, 33 (9): 5057-5069. [33] ZHANG H, GE H, YANG J, et al., 2021. Review of vehicle routing problems: Mod els, classification and solving algorithms[J]. Archives of Computational Methods in Engineering: 1-27. [34] ZHANG Q, LI H, 2007. Moea/d: A multiobjective evolutionary algorithm based on decomposition[J]. IEEE Transactions on evolutionary computation, 11(6): 712-731. [35] ZITZLER E, KÜNZLI S, 2004. Indicator-based selection in multiobjective search [C]//International conference on parallel problem solving from nature. Springer: 832-842. [36] ZITZLER E, THIELE L, 1998. Multiobjective optimization using evolutionary algo rithms—a comparative case study[C]//International conference on parallel problem solving from nature. Springer: 292-301. [37] ZITZLER E, DEB K, THIELE L, 2000. Comparison of multiobjective evolutionary algorithms: Empirical results[J]. Evolutionary computation, 8(2): 173-195. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91428 | - |
| dc.description.abstract | 當前B2C電子商務正處於蓬勃發展的階段，特別是在近幾年COVID-19疫情的影響下，宅經濟迅速興起，引發了消費者對於網上購物的需求增長，這個趨勢迫使電商業者不得不積極關注物流管理的效率和成本，實現更高效的產品配送以應對日益增長的訂單數量和物流挑戰。
本研究的目的在於解決都市區車輛途程問題，在已知車輛時窗及容量限制下，找出能夠最小化車輛數及時窗懲罰成本的可行解，相較單目標問題，多目標問題因為存在非支配關係追求的是解集的多樣性與均衡性，而不僅僅是單一最佳解，意味著求解將變得更加複雜且困難。 多目標問題的複雜性使得傳統方法無法很有效率的解決，本研究使用M-MOEA/D演算法，演算法混合基於分解的MOEA/D演算法與同時進行全局和局部搜索的Memetic演算法，將複雜的多目標問題分解成更容易處理的多個單目標問題同步進行優化，同時加入局部搜索對解作更進一步優化。 最後實際使用國內知名B2C電商訂單資料進行模擬，嘗試不同參數設置進行敏感度分析，最終說明單位車輛成本與單位懲罰成本對業者顧客滿意度與投入成本的影響，提供電商業者一個消費者補貼策略的參考依據。 | zh_TW |
| dc.description.abstract | B2C e-commerce is booming, spurred by factors like the COVID-19 pandemic's home economy trend, driving up the demand for online shopping. This surge has intensified the need for efficient logistics to manage the growing order volume and associated challenges.
This study focuses on tackling the urban vehicle routing problem within predefined vehicle time windows and capacity limits. The aim is to find solutions that minimize vehicle count and penalty costs due to time window violations. Unlike single-objective problems, multi-objective problems aim for a diverse solution set, complicating the solving process. To address this complexity, the M-MOEA/D algorithm combines decomposition-based MOEA/D with Memetic for global and local search. This strategy simplifies the problem by breaking it into manageable single-objective sub-problems optimized concurrently, enriched by local search. Finally, real-world order data from a well-known domestic B2C e-commerce platform are employed for simulations. Sensitivity analyses are conducted under various parameter settings. The study concludes by illustrating the impact of unit vehicle cost and unit penalty cost on customer satisfaction and operational cost for businesses, offering valuable insights for e-commerce companies in devising consumer subsidy strategies. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-01-26T16:27:44Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-01-26T16:27:44Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員審定書 i
摘要 iii Abstract v 目錄 vii 圖目錄 xi 表目錄 xiii 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 3 1.3 研究流程及架構 3 1.4 論文架構 4 第二章 文獻探討 5 2.1 車輛途程問題 (Vehicle Routing Problem, VRP) 5 2.1.1 問題定義 5 2.1.2 精確演算法 (Exact algorithm) 6 2.1.3 啟發式演算法 (Heuristic algorithm) 6 2.2 多目標最佳化問題 (Multiple Objective Optimization Problem, MOOP) 12 2.2.1 問題定義 12 2.2.2 求解方法 14 2.2.3 評價指標 15 2.3 多目標時窗限制車輛途程問題 (Multiple Objective Vehicle Routing Problem, MO-VRPTW) 15 2.3.1 求解方法 16 第三章 模型建構 19 3.1 問題描述 19 3.2 模型假設 21 3.3 數學模型 21 3.3.1 參數定義 21 3.3.2 目標及限制式定義 22 3.4 求解方法 24 3.4.1 個體表達和初始化 27 3.4.2 適應度函數 (Fitness function) 28 3.4.3 交叉操作 (CrossOver) 28 3.4.4 突變操作 (Mutation) 29 3.4.5 局部搜索 (Local search) 30 3.4.6 族群選擇 (Selection) 31 3.4.7 評價指標 (Benchmark) 31 第四章 個案資料分析 33 4.1 資料前處理 33 4.1.1 資料清理 33 4.1.2 估計行政區服務時間 34 4.1.3 估計跨行政區旅行時間 35 4.2 個案參數設定 36 4.3 個案資料求解 37 4.3.1 初始解 38 4.3.2 最終解 (初始解改善) 38 4.4 敏感度分析 41 4.5 分析結論 43 第五章 結論與建議 45 5.1 結論與管理意涵 45 5.2 研究貢獻 45 5.3 研究限制 46 5.4 延伸研究方向 47 參考文獻 49 附錄 A — VRP 問題分類 55 附錄 B — 遺傳操作虛擬碼 57 B.1 CrossOver—SBX 57 B.2 Mutation—Reallocation & Reposition 59 附錄 C — 族群選擇理論推導證明 61 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 車輛途程問題 | zh_TW |
| dc.subject | 局部搜索 | zh_TW |
| dc.subject | 多目標最佳化 | zh_TW |
| dc.subject | M-MOEA/D演算法 | zh_TW |
| dc.subject | B2C電子商務配送模型 | zh_TW |
| dc.subject | Multi-objective Optimization | en |
| dc.subject | B2C E-commerce Distribution Model | en |
| dc.subject | M-MOEA/D Algorithm | en |
| dc.subject | Vehicle-Routing Problem | en |
| dc.subject | Local Search | en |
| dc.title | 考量多目標最佳化下 B2C 電子商務配送模型之研究 | zh_TW |
| dc.title | A Study on B2C E-commerce Distribution Model Considering Multi-objective Optimization | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林我聰;王孔政 | zh_TW |
| dc.contributor.oralexamcommittee | Woo-Tsong Lin;Kung-Jeng Wang | en |
| dc.subject.keyword | B2C電子商務配送模型,M-MOEA/D演算法,車輛途程問題,局部搜索,多目標最佳化, | zh_TW |
| dc.subject.keyword | B2C E-commerce Distribution Model,M-MOEA/D Algorithm,Vehicle-Routing Problem,Local Search,Multi-objective Optimization, | en |
| dc.relation.page | 61 | - |
| dc.identifier.doi | 10.6342/NTU202304578 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2024-01-02 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 商學研究所 | - |
| 顯示於系所單位： | 商學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-112-1.pdf 未授權公開取用 | 1.87 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。