臺灣博碩士論文加值系統

因近年來電子商務快速發展，消費者改變消費型態，使得許多實體商店轉換經營型態，從生鮮食品到家電用品，甚至傳統書店等，皆開始由原本的實體店面轉為網路經營，物流中心訂單也從過去的少樣多量，轉變為多樣少量。且為了滿足顧客需求，許多業者開始追求24小時快速到貨，使物流中心面臨更多挑戰。傳統物流中心除了訂單從少樣多量變成多樣少量外，還有到貨模式的不規則、需多次揀貨，和商品數量大量等問題產生，基本的倉儲管理系統無法有效管理電子商務類型訂單，因此需要額外的分析技術及智能系統加以增強，倉儲系統才能有效運行。本研究是透過文獻探討的方式，參考了Shiau & Huang(2020)先前的研究案例，並以大數據(Big Data)中的分群(Clustering)方法，為網路書店背後的第三方物流倉庫設計訂單計算方法，以及使揀貨流程更為快速之物流系統，在此物流倉庫中的所有貨品，是使用多儲位作法，儲存貨品的方式為隨機存儲(Randomized Storage)，揀取貨品方式為人至物(Picker-to-Stock)、訂單為批次揀取(Batch Picking)，以如此的物流中心做為研究之場域，並以揀貨台車為基礎，設計一個波次揀貨的揀貨規劃與排程系統：在出貨的時間點之前，可快速揀取倉庫中相鄰貨品，並且在揀貨完成後，可直接包裝顧客貨品。

Due to the rapid development of e-commerce in recent years, consumers have changed their consumption patterns, resulting in many physical stores changing business forms, from fresh food to household appliances, and even traditional bookstores, from original physical stores to online operations, and logistics center orders have also changed from few in the past to a small number. In order to meet the needs of customers, many operators began to pursue 24-hour fast arrival, making the logistics center face more challenges. In addition to changing orders from small to small, traditional logistics centers also have irregular arrival modes, multiple picks, and large quantities of goods. Basic warehousing management systems cannot effectively manage e-commerce orders, so additional analysis technologies and intelligent systems are needed to enhance warehousing. Only the system can operate effectively. This study is based on the previous research cases of Shiau & Huang through literature discussion, and uses the clustering method in Big Data to design an order calculation method for the third-party logistics warehouse behind the online bookstore, and to make the picking process more For a fast logistics system, all goods in this logistics warehouse use the multi-storage method. The way to store goods is Randomized Storage, picking goods is Pick-to-Stock, and orders are picked up in batches 一 Batch Picking, with such a logistics center as the research site, and based on picking trollies, design a picking planning and scheduling system for wave picking: adjacent goods in the warehouse can be quickly picked up before the time of shipment, and customer goods can be packed directlyafter picking is completed product.

摘要 i
ABSTRACT ii
致謝 iv
目錄 v
表目錄 vii
圖目錄 viii
第一章 緒論 1
1.1案例說明 1
1.2隨機儲存倉庫特性與目的 4
1.3波次揀貨 6
1.4在隨機存儲倉庫中的波次揀貨 7
1.5 在隨機存儲倉庫中使用分群 10
1.6研究目的 13
第二章 文獻回顧 14
2.1揀貨方式及策略 14
2.1.1揀貨方法 14
2.1.2揀貨策略 14
2.2儲存策略 16
2.3路徑策略 18
2.4揀貨規劃方法 20
2.2.聚類算法 24
2.3.隨機存儲倉儲案例 26
第三章 研究方法 35
3.1研究設計 35
3.2啟發式演算法 37
3.3 實驗結果及分析 39
3.3.1單一儲存位置資料分析 39
3.3.2 多儲存位置分析 45
3.4路徑規劃 50
3.4.1第一批次路徑 50
3.4.2第二批次路徑 50
3.4.3第三批次路徑 51
3.4.4第四批次路徑 51
3.4.5第五批次路徑 52
3.4.6第六批次路徑 52
3.4.7第七批次路徑 53
3.4.8第八批次路徑 53
3.4.9第一批次路徑(多儲位) 54
3.4.10第二批次路徑(多儲位) 54
3.4.11第三批次路徑(多儲位) 55
3.4.12第四批次路徑(多儲位) 55
3.4.13第五批次路徑(多儲位) 56
3.4.14第六批次路徑(多儲位) 56
3.4.15第七批次路徑(多儲位) 57
3.4.16 第八批次路徑(多儲位) 57
3.5結果與分析 58
第四章 結論 59
參考文獻 61
中文部份 61
英文部份 6

中文部分
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https://hdl.handle.net/11296/75cht8
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