類 Kiva 系統之Pod 分配與品項分配之相關問題探討

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：33

、訪客IP：3.140.198.12

姓名

何東益(Tung-Yi Ho) 查詢紙本館藏

畢業系所

工業管理研究所

論文名稱

類 Kiva 系統之Pod 分配與品項分配之相關問題探討

相關論文

★ 佈置變更專案工程的執行研究－以H公司研發單位為例	★ MIL-STD-1916、MIL-STD-105E與結合製程能力指標之抽樣檢驗計畫
★ 建構客戶導向的製造品質資訊系統--以某筆記型電腦專業代工廠商為例	★ GMP藥廠設施佈置規劃的探討--以E公司為研究對象
★ 應用Fuzzy c-Means演算法之物流中心位址決策模式研究	★ 品質資訊系統之規劃與建構 -- 以某光碟製造公司為研究對象
★ 從製程特性的觀點探討生產過程中SPC管制圖監控運用的適切性 -- 以Wafer Level 封裝公司為例	★ 六標準差之應用個案研究-以光學薄膜包裝流程改善為例
★ 利用六標準差管理提昇中小企業之製程品質-以錦絲線添加防銹蠟改善為例	★ 專業半導體測試廠MES 系統導入狀況、成果及問題之探討-以A 公司為例
★ 以RFID技術為基礎進行安全管理導入－以A公司為例	★ 如何提昇產品品質及降低成本—以光碟壓片廠A公司為例
★ ERP導入專案個案分析—以半導體封裝廠A公司為例	★ 石英元件製造業之延遲策略應用— 以T公司為研究對象
★ 十二吋晶圓廠自動化搬運系統規劃與導入—以A公司為例	★ 半導體封裝產業之生產革新改善活動-A半導體股份有限公司導入經驗探討-

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

隨著工業4.0與物聯網的快速興起下零售業重心逐漸從實體走向電子商務，許多物流中心為了及時滿足顧客需求，將物流中心轉為自動化與智慧化發展進而提升競爭力，才能及時滿足的提供現今市場的各種需求。
全球龍頭零售商¬-亞馬遜網路商店（Amazon.com）成立亞馬遜第八代物流中心導入Kiva System採用大量的機器人、物聯網及工業4.0等技術，其中最重要的改革是利用Kiva機器人（Kiva Robot）將貨架（Pod）送往揀貨人員的「貨到人」揀貨方式。此舉顛覆了傳統物流作業模式，使得揀貨員節省下移動到揀貨區的時間，減少大量的人力浪費及提高物流作業效率，也對整個物流業造成一個重大的改革。
本研究延續並修正宋狄軒（2017）研究，並修正其內容包括：1.將調整訂單及Pod品項分配的品項種類數及數量，使其確保Pod能滿足訂單之需求；2.修改Pod補貨作業模式，補貨時重新分配Pod的品項種類及數量，使 Pod上品項組合更有多樣性；3.修正揀貨作業時間及調整訂單出貨期設置，其設置能更符合實際狀況；4修正部分法則錯誤。目的為使其研究結果能更接近真實物流中心並探討在類 Kiva 系統Pod分配之揀貨站挑選、Pod分配及品項分配的問題，並觀察宋狄軒（2017）的單屬性表現提出多屬性Fuzzy評估法則，透過軟體模擬分析在不同的績效指標搭配不同的實驗因子，期望找出最佳的法則搭配能使Kiva系統達到最佳效能，減少不必要的浪費。

摘要(英)

With the rapid rise of Industry 4.0 and the Internet of Things, the focus of the retail industry has gradually moved from physical to e-commerce.In order to meet the needs of customers in time, many logistics centers turn the logistics center into automation and intelligent development to enhance competitiveness, and can timely meet the various needs of the market.
Amazon established Amazon′s eighth-generation logistics center to introduce Kiva System using a large number of technologies such as robotics, the Internet of Things and Industry 4.0. One of the most important reforms is "goods to person" picking method that Kiva Robots to send the shelves (Pod) to the picker. This move subverts the traditional logistics operation mode, which enables the picker to save time moving to the picking ar-ea, reduce a large amount of manpower waste and improve the efficiency of logistics op-erations, and also cause a major reform of the entire logistics industry
This study continues and modifies Sung (2017) research.The aim is to get the results closer to the real logistics center and to investigate “the selection of workstation to Pod assign”, “Pod allocation” and “SKU allocation” issues in similar Kiva system. Observing the single attribute heuristic algorithms performance of Sung (2017), this study proposes multi-attribute fuzzy evaluation algorithms. Through software simulation analysis, dif-ferent performance indicators are combined with different experimental factors, and ex-pected to find the best rule to achieve the best performance and reduce unnecessary waste.

關鍵字(中)

★ 物流
★ Kiva System
★ 物聯網
★ 工業4.0
★ Fuzzy evaluation

關鍵字(英)

★ Logistics
★ Kiva System
★ IoT
★ Industry 4.0
★ Fuzzy evaluation

論文目次

摘要 i
Abstract ii
目錄 iii
圖目錄 vi
表目錄 viii
第一章緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 2
1.4 研究環境 3
1.5 論文架構 4
第二章文獻探討 6
2.1 物流 6
2.1.1 物流的定義 6
2.1.2 物流中心之類型及作業流程 7
2.2 揀貨作業 9
2.2.1 揀貨作業方式 10
2.2.2 揀貨路徑策略 12
2.3 Kiva系統 15
2.3.1 Kiva系統之介紹 15
2.3.2 Kiva系統之相關研究 19
第三章研究方法 22
3.1 符號及變數定義 22
3.2 類Kiva系統作業流程 23
3.2.1 揀貨工作站作業流程 23
3.2.2 Pod分配流程 25
3.3 各研究議題之方法整理 28
3.4 Pod 分配之揀貨工作站挑選法則 29
3.4.1 隨機挑選法則 29
3.4.2 「已分配Pod數最小」法則 29
3.4.3 「已分配Pod之總剩餘揀貨時間最小」法則 30
3.4.4 「未滿足訂單品項總數最大」法則 31
3.4.5 「未滿足訂單品項總數最小」法則 31
3.4.6 「訂單的總寬放時間最少」法則 32
3.4.7 「訂單的平均寬放時間最少」法則 33
3.4.8 「最小總寬放時間與尚未滿足揀貨的訂單品項總數之比值」法則 33
3.4.9 揀貨工作站之多屬性Fuzzy評估法則 34
3.5 類Kiva系統中 Pod 分配法則 37
3.5.1 隨機法則 37
3.5.2 「可滿足最多（訂單）品項總數的 Pod 優先」法則 37
3.5.3 「可滿足最多品項種類數的 Pod 優先」法則 38
3.5.4 「距離揀貨工作站最近的 Pod 優先」法則 39
3.5.5 「有品項種類被 Pod 滿足之訂單的平均出貨時間最近優先」法則 39
3.5.6 Pod多屬性Fuzzy評估法則 40
3.6 類Kiva系統中品項分配法則 42
3.6.1 隨機法則 43
3.6.2 「出貨期最早之訂單愈優先」法則 43
3.6.3 「對該品項種類 IT* 需求量愈大之訂單愈優先」法則 44
3.6.4 「對該品項種類 IT* 需求量愈小之訂單愈優先」法則 45
3.6.5 「寬鬆時間愈小之訂單愈優先」法則 46
3.6.6 「未滿足品項種類數愈多之訂單愈優先」法則 47
3.6.7 「未滿足品項種類數愈少之訂單愈優先」法則 48
3.6.8 「未滿足品項總數愈多之訂單愈優先」法則 50
3.6.9 「未滿足品項總數愈少之訂單愈優先」法則 51
3.6.10 訂單多屬性Fuzzy評估法則 52
第四章實驗結果與分析 55
4.1 模擬實驗設計 55
4.1.1 環境設定 55
4.1.2 訂單設定與其他設定 56
4.1.3 實驗假設 57
4.1.4 實驗因子組合 58
4.2 績效評估準則 60
4.3 統計分析 60
4.3.1 依「總系統執行時間」為績效指標 62
4.3.2 依「訂單流程時間」為績效指標 67
4.3.3 依「差異時間」為績效指標 72
4.3.4 依「延遲時間」為績效指標 77
4.3.5 依「總 Pod 行走時間」為績效指標 83
4.3.6 依「Pod在工作站平均等待時間」為績效指標 88
4.4 實驗結論 93
第五章結論與後續建議 96
5.1 研究結論 96
5.2 未來研究建議 97
參考文獻 98
中文文獻 98
英文文獻 100

參考文獻

中文文獻
1. 何山田，2002，「低溫物流中心之規劃設計-以家風低溫物流中心為例」，國立中山大學高階經營碩士學程專班，碩士論文。
2. 宋狄軒，2017，「類 Kiva 系統的「Pod 分配之揀貨站挑選」、「Pod 分配於揀貨站」與「品項分配至訂單」問題之探討，國立中央大學工業管理研究所，碩士論文。
3. 李建興，2014，Amazon用機器人大軍徹底顛覆物流配送作業，iThome，取得日期：02/04/2019，取自：https：//www.ithome.com.tw/news/92974。
4. 周曉光、張喜妹、劉玉坤，2015，一種基於移動機器人的配送中心柔性揀選系統，物流技術，34（7），238-240。
5. 美國SOLE國際物流協會台灣分會、台灣全球運籌發展分會，2013，物流與運籌管理。新北；美國SOLE國際物流協會台灣分會。
6. 陳暉江，2004，「具兩條以上橫向走道之物流中心揀貨路徑規劃研究」，國立中央大學工業管理研究所，碩士論文。
7. 趙廷偉，2012，「具語音引導揀貨系統之揀貨作業的執行與績效」，國立中央大學工業管理研究所，碩士論文
8. 趙義隆，1991，「物流中心的策略定位」，物流管理系列學會研討會暨座談會，1991 年。
9. 億歐網，2016，解讀丨馬雲口中的「新零售」到底為何物？，每日頭條，取得日期：04/04/2019，取自：https://kknews.cc/tech/ajopov.html。
10. 鄭炳坤，2005，「RFID於物流中心應用之探討」，中原大學工業工程研究所，碩士論文。
11. 賴廷彰，2002，「物流中心區位選擇影響因素之研究」，國立臺北大學都市計劃研究所，博士論文。

英文文獻
1. Azadeh, K., de Koster, R., & Roy, D. (2017). Robotized warehouse systems: Devel-opments and research opportunities(No. ERS-2017-009-LIS).
2. Bartholdi, J. J., & Hackman, S. T. (2008). Warehouse & Distribution Science: Re-lease 0.89 . Atlanta, Ga.: The Supply Chain and Logistics Institute.
3. Battini, D., Calzavara, M., Persona, A., & Sgarbossa, F. (2017). Additional effort es-timation due to ergonomic conditions in order picking systems. International Journal of Production Research, 55(10), 2764-2774.
4. Bozer, Y. A., & Aldarondo, F. J. (2018). A simulation-based comparison of two goods-to-person order picking systems in an online retail setting. International Journal of Production Research, 56(11), 3838-3858.
5. Cergibozan, Ç., & Tasan, A. S. (2016). Order batching operations: an overview of classification, solution techniques, and future research. Journal of Intelligent Manu-facturing, 1-15.
6. Chen, F., Wang, H., Xie, Y., & Qi, C. (2016). An ACO-based online routing method for multiple order pickers with congestion consideration in warehouse. Journal of Intelligent Manufacturing, 27(2), 389-408.
7. Cheng, C. Y., Chen, Y. Y., Chen, T. L., & Yoo, J. J. W. (2015). Using a hybrid ap-proach based on the particle swarm optimization and ant colony optimization to solve a joint order batching and picker routing problem. International Journal of Production Economics, 170, 805-814.
8. Chirici, L., & Wang, K. S. (2014). Tackling the storage problem through genetic al-gorithms. Advances in Manufacturing, 2(3), 203-211.
9. Cho, G. S. (2018). A Study on Establishment of Smart Logistics Center based on Lo-gistics 4.0. The Journal of Multimedia Information System, 5(4), 265-272.
10. Correll, N., Bekris, K. E., Berenson, D., Brock, O., Causo, A., Hauser, K., ... & Wurman, P. R. (2018). Analysis and observations from the first amazon picking challenge. IEEE Transactions on Automation Science and Engineering, 15(1), 172-188.
11. De Koster, R., Le-Duc, T., & Roodbergen, K. J. (2007). Design and control of ware-house order picking: A literature review. European journal of operational re-search, 182(2), 481-501.
12. Đukić, G., Česnik, V., & Opetuk, T. (2010). Order-picking methods and technologies for greener warehousing. Strojarstvo: časopis za teoriju i praksu u strojarstvu, 52(1), 23-31.
13. Eppner, C., Höfer, S., Jonschkowski, R., Martín-Martín, R., Sieverling, A., Wall, V., & Brock, O. (2016, June). Lessons from the amazon picking challenge: Four aspects of building robotic systems. In Robotics: Science and Systems.
14. Grosse, E. H., Glock, C. H., Jaber, M. Y., & Neumann, W. P. (2015). Incorporating human factors in order picking planning models: framework and research opportuni-ties. International Journal of Production Research, 53(3), 695-717.
15. Guizzo, E. (2008). Three engineers, hundreds of robots, one warehouse. IEEE spec-trum, 45(7), 26-34.
16. Henn, S., & Wäscher, G. (2012). Tabu search heuristics for the order batching prob-lem in manual order picking systems. European Journal of Operational Re-search, 222(3), 484-494.
17. Hong, S., Johnson, A. L., & Peters, B. A. (2012). Large-scale order batching in paral-lel-aisle picking systems. IIE Transactions, 44(2), 88-106.
18. Hong, S., Johnson, A. L., & Peters, B. A. (2015). Quantifying picker blocking in a bucket brigade order picking system. International Journal of Production Econom-ics, 170, 862-873.
19. Kulak, O., Sahin, Y., & Taner, M. E. (2012). Joint order batching and picker routing in single and multiple-cross-aisle warehouses using cluster-based tabu search algo-rithms. Flexible services and manufacturing journal, 24(1), 52-80.
20. Lam, C. H., Choy, K. L., Ho, G. T., & Lee, C. K. M. (2014). An order-picking opera-tions system for managing the batching activities in a warehouse. International Journal of Systems Science, 45(6), 1283-1295.
21. Lamballais, T., Roy, D., & De Koster, M. B. M. (2017). Estimating performance in a robotic mobile fulfillment system. European Journal of Operational Re-search, 256(3), 976-990.
22. Lamballais, T., Roy, D., & De Koster, M. B. M. (2019). Inventory allocation in ro-botic mobile fulfillment systems. IISE Transactions, (just-accepted), 1-22.
23. Le-Duc, T. (2005). Design and Control of Efficient Order Picking Process-es (Doctoral dissertation, Erasmus Research Institute of Management).
24. Lee, C. K. M., Lv, Y., Ng, K. K. H., Ho, W., & Choy, K. L. (2018). Design and appli-cation of Internet of things-based warehouse management system for smart logis-tics. International Journal of Production Research, 56(8), 2753-2768.
25. Li, J. T., & Liu, H. J. (2016). Design optimization of amazon robotics. Automation, Control and Intelligent Systems, 4(2), 48-52.
26. Liang, C., Chee, K. J., Zou, Y., Zhu, H., Causo, A., Vidas, S., ... & Cheah, C. C. (2015, October). Automated robot picking system for e-commerce fulfillment warehouse application. In The 14th IFToMM World Congress.
27. Lienert, T., Staab, T., & Ludwig, C. F. (2018). Simulation-based Performance Analy-sis in Robotic Mobile Fulfilment Systems. In Proceedings of the 8th International Conference on Simulation and Modeling Methodologies, Technologies and Applica-tions.
28. Lu, W., McFarlane, D., Giannikas, V., & Zhang, Q. (2016). An algorithm for dynamic order-picking in warehouse operations. European Journal of Operational Re-search, 248(1), 107-122.
29. Merschformann, M., Lamballais, T., de Koster, R., & Suhl, L. (2018). Decision rules for robotic mobile fulfillment systems. arXiv preprint arXiv:1801.06703.
30. Musa, A., & Dabo, A. A. A. (2016). A review of RFID in supply chain management: 2000–2015. Global Journal of Flexible Systems Management, 17(2), 189-228.
31. Musa, A., Gunasekaran, A., Yusuf, Y., & Abdelazim, A. (2014). Embedded devices for supply chain applications: Towards hardware integration of disparate technolo-gies. Expert Systems with Applications, 41(1), 137-155.
32. Nigam, S., Roy, D., Koster, R. D., & Adan, I. (2014). Analysis of class-based storage strategies for the mobile shelf-based order pick system.
33. Parikh, P. J., & Meller, R. D. (2008). Selecting between batch and zone order picking strategies in a distribution center. Transportation Research Part E: Logistics and Transportation Review, 44(5), 696-719.
34. Petersen, C. G. (1999). The impact of routing and storage policies on warehouse effi-ciency. International Journal of Operations & Production Management, 19(10), 1053-1064.
35. Petersen II, C. G. (2000). An evaluation of order picking policies for mail order companies. Production & Operations Management, 9(4), 319-335
36. Petersen, C. G., & Aase, G. (2004). A comparison of picking, storage, and routing policies in manual order picking. International Journal of Production Econom-ics, 92(1), 11-19.
37. Quader, S., & Castillo-Villar, K. K. (2018). Design of an enhanced multi-aisle or-der-picking system considering storage assignments and routing heuristics. Robotics and Computer-Integrated Manufacturing, 50, 13-29.
38. Blake, R. (2019). Amazon′s Push to Augment Workforce with Automation is Pig in Industrial Robotics Python, Retrieved April 20, 2019, from https://www.forbes.com/sites/richblake1/2019/02/24/amazons-push-to-augment-workforce-with-automation-is-pig-in-industrial-robotics-python/#4ce5da5942ea
39. Scholz, A., Schubert, D., & Wäscher, G. (2017). Order picking with multiple pickers and due dates–Simultaneous solution of Order Batching, Batch Assignment and Se-quencing, and Picker Routing Problems. European Journal of Operational Re-search, 263(2), 461-478.
40. Theys, C., Bräysy, O., Dullaert, W., & Raa, B. (2010). Using a TSP heuristic for rout-ing order pickers in warehouses. European Journal of Operational Research, 200(3), 755-763.
41. Thomas, R.J. , Kass, A. , & Davarzani, L. (2014).“Recombination at amazon and kiva systems：balancing human and machine capabilities”. Retrieved April 20, 2019, from: https://www.accenture.com/t20150825T041249__w__/dk-en/_acnmedia/Accenture/Conver-si-on-Assets/DotCom/Documents/Global/PDF/Dualpub_20/Accenture-Impact-Of-Tech-AmazonKiva.pdf
42. Van den Berg, J. P., & Zijm, W. H. (1999). Models for warehouse management: Clas-sification and examples. International journal of production economics, 59(1-3), 519-528.
43. Van Gils, T., Ramaekers, K., Braekers, K., Depaire, B., & Caris, A. (2018). Increasing order picking efficiency by integrating storage, batching, zone picking, and routing policy decisions. International Journal of Production Economics, 197, 243-261.
44. Van Gils, T., Ramaekers, K., Caris, A., & de Koster, R. B. (2018). Designing efficient order picking systems by combining planning problems: State-of-the-art classifica-tion and review. European Journal of Operational Research, 267(1), 1-15.
45. Roush, W. , (2007).Kiva’s Robots Bring New Meaning to Movable Shelves. Retrieved April 20, 2019, from: https://xconomy.com/boston/2007/10/12/kivas-robots-bring-new-meaning-to-movable-shelves/attachment/kiva-mobile-shelving-system/
46. Wulfraat, M. , (2012). Is Kiva Systems a Good Fit for Your Distribution Center. Re-trieved April 20, 2019, from: http://www.mwpvl.com/html/kiva_systems.html
47. Xiang, X., Liu, C., & Miao, L. (2018). Storage assignment and order batching prob-lem in Kiva mobile fulfilment system. Engineering Optimization, 50(11), 1941-1962.
48. Xie, L., Li, H., & Thieme, N. (2018). From simulation to real-world robotic mobile fulfillment systems. arXiv preprint arXiv:1810.03643.
49. Xie, L., Thieme, N., Krenzler, R., & Li, H. (2019). Efficient order picking methods in robotic mobile fulfillment systems. arXiv preprint arXiv:1902.03092.
50. Yuan, Z., & Gong, Y. Y. (2017). Bot-in-time delivery for robotic mobile fulfillment systems. IEEE Transactions on Engineering Management, 64(1), 83-93.
51. Zou, B., Xu, X., & De Koster, R. (2018). Evaluating battery charging and swapping strategies in a robotic mobile fulfillment system. European Journal of Operational Research, 267(2), 733-753.

指導教授

何應欽(Ying-Chin Ho)

審核日期

2019-7-29

推文