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。 ;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.
