本研究欲探討類Kiva系統中之「Pod分配之揀貨工作站挑選」、「Pod分配挑選」及「訂單分配之品項挑選」等派送問題，並且透過模擬軟體提出多種績效指標，用來評估及分析何種因子法則之組合能有最好的表現，期望能找出最佳之法則使類Kiva系統達到最佳效能，減少不必要的浪費。本研究將延續何東益（2019）之研究，觀察何東益（2019）所提出之單屬性法則並發展出一多屬性評估法則，期望多屬性評估法則能在類Kiva系統中有不錯的績效表現。 ;With the advancement of network technology, people′s consumption habits have been greatly changed. Customers can buy goods on the Internet no matter when and where they are, subverting the time and cost of shopping in physical stores. This change in consumption habits also forced the logistics center to make changes in pursuit of a more efficient operation mode. The arrival of Industry 4.0 makes the logistics center intelligent and enhances the competitiveness of the logistics center. Global e-commerce leader Amazon acquired Kiva System in 2012, and made Kiva System the core of its eighth-generation logistics center. The biggest feature of the Kiva System is the Kiva robot, which completes the picking operation in a "goods-to-human" way, which reduces the huge labor cost. This move subverts the previous "human-to-goods" method for picking operations, and also greatly improves The efficiency of the logistics center. This paper intends to explore the delivery problems of " workstation selection", "Pod allocation" and "SKU allocation" in Kiva-like systems, and proposes various performance indicators through simulation software for evaluation and Analyze what combination of factor laws can have the best performance, and expect to find the best law to make the Kiva-like system achieve the best performance and reduce unnecessary waste. This research will continue the research of Ho (2019), observe the single-attribute rule proposed by Ho (2019) and develop a multi-attribute evaluation rule, hoping that the multi-attribute evaluation rule can perform well in Kiva-like systems .
