類似Kiva系統之Pod儲位分配問題探討

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蘇芳儀(Fang-I Su) 查詢紙本館藏

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工業管理研究所

論文名稱

類似Kiva系統之Pod儲位分配問題探討

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摘要(中)

現今電子商務伴隨著工業4.0與物聯網的衝擊下，許多傳統產業為了因應勞動力不足、作業環境惡劣與週期短且少量多樣的產品而被迫轉型。其中許多物流中心為了提升自身競爭力，逐漸將其工廠轉為自動化與智慧化工廠，以即時滿足顧客需求，並快速反應市場變化。
　　美國第一大零售網站—亞馬遜網路購物商城（Amazon）看準未來商機，在2012年引進 Kiva 系統，成立了亞馬遜第八代物流中心，結合物聯網、大數據、雲端及人機協同之工業4.0技術核心，改善物流中心內部自動化倉儲流程。亞馬遜第八代物流中心其中一項突破為 Kiva 機器人（Kiva Robot）載著裝有品項的貨架（Pod）「以貨到人」的作業方式送往揀貨工作站，揀貨人員只需根據訂單揀取正確的品項及數量，這時 Pod 上的品項該如何擺放以降低揀貨時所需花費的時間距離成本，便為一大重點。
　　本研究模擬在類似Kiva系統的作業環境下，探討 Pod 上儲位指派等問題，透過模擬軟體分析在不同儲位法則搭配其他作業流程的績效表現，期望能找到最佳的儲位分配方法，改善工廠內部作業效率，提升系統整體效能。

摘要(英)

E-commerce accompanied by the Industrial 4.0 and Internet Of Things(IOT), many traditional industries were forced to transition in order to react to the labor shortage, poor operating environment and the products belong to short cycle and “Small-Volume, Multiple-Types”. Some of these logistics centers enhance their competitiveness, turning their factories into automated and intelligent plants to meet customer needs and respond to the changes of market rapidly.
Amazon.com, the largest retail site in the United States, sees the future development of business opportunities, imported Kiva System in 2012 and set up the Amazon’s eighth-generation logistics center. The core technique of Industrial 4.0 by combining the IOT, Big Data, Cloud and man-machine coordination to improve internal automated storage process of the logistics center.
One of the breakthroughs of the Amazon’s eighth-generation logistics center was Kiva Robots which were tasked with delivering Pod to work stations. At that time, pickers just only pick up the correct items and quantities by order demand. As a result, how to assign the items to reduce the picking processing time is our major issues.
This study simulates the problem of Pod storage location assignment in the operating environment similar to Kiva system, and analyzes the performance of different assignment rules through simulation software, hoping to find the best storage assignment method to improve internal operation efficiency and enhance the overall system performance.

關鍵字(中)

★ 工業4.0
★ Kiva 系統
★ 儲位指派

關鍵字(英)

★ Industrial 4.0
★ Kiva System
★ Storage assignment

論文目次

摘要 i
Abstract ii
圖目錄 vi
表目錄 viii
第一章緒論 1
1.1 研究背景 1
1.2 亞馬遜第八代物流中心 2
1.3 研究動機 3
1.4 研究目的 4
1.5 研究環境 4
1.6 論文架構 5
第二章文獻探討 7
2.1 物流 8
2.1.1 物流的定義 9
2.1.2 物流中心的定義 10
2.1.3 物流中心的作業流程 10
2.1.4 物流中心的變革 11
2.1.5 倉儲管理系統 13
2.1.6 物聯網應用於倉儲作業系統 14
2.2 Kiva系統 15
2.2.1 Kiva系統之介紹 16
2.2.2 Kiva系統的倉儲佈置 19
2.2.3 Kiva Robot的作業流程 20
2.2.4 Kiva系統人工智慧 20
2.2.5 Kiva系統的特色 21
2.2.6 Kiva系統與傳統物流中心之比較 23
2.2.7 Kiva系統的資源分配 24
2.3 儲位規劃 25
2.3.1 儲存策略 25
2.3.2 儲位指派 28
2.3.3 儲存佈置 34
第三章研究方法 37
3.1 研究方法之整理 37
3.2 符號及變數定義 38
3.3 第一類法則 39
3.3.1 第一類之一次分配一個方法 39
3.3.2 第一類之一次分配Q1個方法 42
3.3.3 第一類之一次分配Q2個方法 43
3.3.4 第一類之一次分配Q3個方法 45
3.4 第二類法則 47
3.4.1 第二類之一次分配1個方法 47
3.4.2 第二類之一次分配Q1個方法 50
3.4.3 第二類之一次分配Q2個方法 51
3.4.4 第二類之一次分配Q3個方法 53
3.5 第三類法則 55
3.5.1 第三類之一次分配1個方法 55
3.5.2 第三類之一次分配Q1個方法 58
3.5.3 第三類之一次分配Q2個方法 61
3.5.4 第三類之一次分配Q3個方法 64
3.6 第四類法則 67
3.6.1 第四類之一次分配1個方法 68
3.6.2 第四類之一次分配Q1個方法 70
3.6.3 第四類之一次分配Q2個方法 72
3.6.4 第四類之一次分配Q3個方法 75
3.7 第五類法則 77
3.7.1 第五類之一次分配1個方法 77
3.7.2 第五類之一次分配Q1個方法 80
3.7.3 第五類之一次分配Q2個方法 83
3.7.4 第五類之一次分配Q3個方法 86
3.8 第六類法則 89
3.8.1 第六類之一次分配1個方法 90
3.8.2 第六類之一次分配Q1個方法 91
3.8.3 第六類之一次分配Q2個方法 94
3.8.4 第六類之一次分配Q3個方法 96
第四章模擬實驗與分析 98
4.1 前言 98
4.2 模擬實驗 98
4.2.1 環境設計 98
4.2.2 環境假設 100
4.2.3 實驗因子總覽 101
4.3 績效評估準則 103
4.4 統計分析 103
4.4.1. 依『訂單流程時間（Order Flow Time）』為績效評估值 104
4.4.1.1. 各類法則之比較（以Order Flow Time為績效值） 104
4.4.1.2. 各類別與分配個數之比較（以Order Flow Time為績效值） 106
4.4.1.3. 兩兩類別與分配數量之比較（以Order Flow Time為績效值） 110
4.4.1.3.1. 一、二類法則分配數量之比較（以Order Flow Time為績效值） 110
4.4.1.3.2. 三、五類法則分配數量之比較（以Order Flow Time為績效值） 113
4.4.1.3.3. 四、六類法則分配數量之比較（以Order Flow Time為績效值） 114
4.4.2. 依『系統執行時間（Total System Time; TST）』為績效評估值 116
4.4.2.1. 各類法則之比較（以TST為績效值） 117
4.4.2.2. 各類別與分配個數之比較（以TST為績效值） 118
4.4.2.3. 兩兩類別與分配數量之比較（以TST為績效值） 123
4.4.2.3.1. 一、二類法則分配數量之比較（以TST為績效值） 123
4.4.2.3.2. 三、五類法則分配數量之比較（以TST為績效值） 125
4.4.2.3.3. 四、六類法則分配數量之比較（以TST為績效值） 127
4.5實驗結論 129
第五章結論與後續研究建議 131
5.1 研究結論 131
5.2 未來研究建議 131
參考文獻 133
中文文獻 133
英文文獻 134

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指導教授

何應欽(Ying-Chin Ho)

審核日期

2017-7-26

推文