研究生: |
吳沛澄 Pei-Cheng Wu |
---|---|
論文名稱: |
智動化揀貨系統之揀貨流程最佳化研究 Optimization of Order Picking Process in Robotic Mobile Fulfillment System |
指導教授: |
周碩彥
Shuo-Yan Chou 郭伯勳 Po-Hsun Kuo |
口試委員: |
陳振明
Chen, Jen-Ming |
學位類別: |
碩士 Master |
系所名稱: |
管理學院 - 工業管理系 Department of Industrial Management |
論文出版年: | 2022 |
畢業學年度: | 110 |
語文別: | 英文 |
論文頁數: | 62 |
中文關鍵詞: | 智動化揀貨系統 、訂單揀貨系統 、元啟發式演算法 、區域搜尋 、模擬 |
外文關鍵詞: | Robotic Mobile Fulfillment System (RMFS), Order Picking, Metaheuristic, Local search, Simulation |
相關次數: | 點閱:474 下載:11 |
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智動化揀貨系統 (Robotic Mobile Fulfillment System, RMFS) 是眾所周
知且常應用於電子商務業務中的訂單揀貨系統。但是,該系統還有很多方面
需要改進。最關鍵的問題之一是關於訂單揀貨流程。 RMFS中訂單揀貨流程
的決策問題可以分為兩個:揀貨訂單指派(POA)和揀貨貨架選擇(PPS)。
大多數文獻解決了該問題的靜態版本。為了在不同的情況下有效地執行訂單
揀選,本研究提出了一種啟發式方法來解決即時的問題。所提出的方法根據
當前情況動態優化訂單批量大小。與大多數文獻最小化貨架訪問相比,所提
出模型的目標函數是最大化實現高吞吐量的重要指標: Pile-on。最大化每批
次的Pile-on可以提高揀貨效率,降低系統成本。本研究探討了兩種類型的
Pile-on。一個是常用於許多文獻的Pile-on——每次貨架訪問揀貨站可提供的
SKU 數量。另一種是額外考慮完成訂單數量的版本。為了有效地解決問題,
本研究提出了一種應用貪婪隨機自適應搜索過程(Greedy Randomized
Adpative Search Procedure, GRASP)元啟發式的啟發式算法。本研究將所提
出的訂單揀選模型實現於代理人模擬系統,並使用了數個指標來測試所提出
方法的性能以測試其有效性。結果表明,所提出的方法在大多數指標上都顯
著優於基準方法,除此之外,本研究發現一項新的指標與訂單吞吐量有很強
的相關性。
Firstly, I would like to express my sincerest gratitude to my advisor, Prof.
Shuo-Yan Chou, who has supported and guided me throughout my research and
thesis. His ideas, kindness, advice, and passion always inspire and motivate me to
enhance my work and achieve a great outcome. I would also like to acknowledge
Prof. Po-Hsun Kuo as my co-advisor and Prof. Jen-Ming Chen as my thesis defense
committee for their encouragement, insightful comments, evaluation, and
suggestions for my research.
Secondly, I would also like to give my appreciation to all my
labmates/friends in the Center of IoT Innovation (CITI), especially Kiva teammates:
Moritz, Agnes, Chaterine, Edwin, Tina, Rasyid, Dennis, Ben, Ian, David, and all
who involves, for their friendliness, kindness, and support during my work in this
project these past two years. And I also want to give immense gratitude to others
who provide me with lots of help, patience, guidance, care, and support: Indie, Rafi,
Ryanda, Joe, Phoebe, Molly, Kevin, and all other members. And I would also like
to thank my friends who always support, love, and encourage me, especially my
lovely friend 9mbb. Furthermore, I must express my profound gratitude to my
parents and siblings for providing me with unfailing support and continuous
encouragement throughout my years of study and through the process of
researching and writing this thesis. This accomplishment would not have been
possible without them. Thank you.
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