類似Kiva系統之Pod分配於揀貨工作站與品項分配至訂單的相關問題探討

NCUIR > School of Management at National Central University > Graduate Institute of Industrial Management > Electronic Thesis & Dissertation > Item 987654321/71226

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/71226

Title:	類似Kiva系統之Pod分配於揀貨工作站與品項分配至訂單的相關問題探討
Authors:	翁佳蓮;Weng-Chia-Lien
Contributors:	工業管理研究所
Keywords:	智慧物流系統;揀貨作業;智慧型機器人;品項分配;訂單順序;Smart factory;picking;smart robot;item distribution;order sequence
Date:	2016-07-26
Issue Date:	2016-10-13 12:13:34 (UTC+8)
Publisher:	國立中央大學
Abstract:	最近幾年台灣的人力成本上揚，業者多仰賴中國大陸或其他東南亞國家之人力及土地成本，如今受到全球少子化之影響，亞洲地區也面臨人口衰退的現象，即使是將工廠搬到中國大陸或轉移到東南亞地區的國家，有時候勞動人口也無法滿足對人力的需求，既然無法仰賴傳統人力，自然就需要更聰明的機器人。由過去的文獻中發現，以往針對物流環境的相關研究大多在一般揀貨環境的揀貨路徑規劃、儲位擺放等議題，對於智慧物流工廠中所牽涉到Pod如何分配至揀貨工作站與品項分配至訂單的相關問題研究較缺乏，故本研究將針對智慧物流工廠中「Pod如何分配至揀貨工作站」與「品項如何分配至訂單」的相關問題進行探討，期望可以提高訂單完成的速度。當訂單抵達揀貨工作站後，即會開始分配訂單到每個揀貨工作站中的儲存架空格內，此時，揀貨作業也會同時進行。本研究將實驗流程分為兩階段，第一階段探討如何分配Pod到揀貨工作站，第二階段則是執行品項分配至訂單的問題，並搭配三種不同的Pod要求條件之策略，透過模擬實驗，期望在此模擬環境中找出最佳的因子組合，並提出總系統執行時間及總揀貨時間這兩個績效指標，以了解不同因子組合的績效表現。 ;In recent years, the labor costs rises in Taiwan. The company dependent on lower labor costs in China or other Southeast Asian countries. Nowadays, because of the global low birth rate, Asia is also facing the population of birds decreased greatly recent years. Sometimes other countries can’t satisfy the labor needs, so we need smart robot to help us do a lot of things. From the literature in the past, most of relevant research on the logistics are focus on general picking, route planning and storages management of storing place. Lack of research about the Pod distribution to work station and pick items to order in smart factory. This study focuses on Pod distribution and item distribution. We propose two decision problems “the selection rules of Pod in work station”, “the assignment rules of items in order sequence”. The data is obtained by simulation software, Arena. We analyze the performance index such as total system time（TST）, total picking time（TPT）in our environment. Finally, the best combinations of rules under different performance index are discovered. It’s observed that the best combination under performance index, TST and TPT, would be PSIOAO × NSIM × P3, which can make the best enhance in the system.
Appears in Collections:	[Graduate Institute of Industrial Management] Electronic Thesis & Dissertation

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