Li XHua GHuang AJIUH-BIING SHEUCheng T.C.EHuang F.2021-08-312021-08-31202013665545https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096663929&doi=10.1016%2fj.tre.2020.102158&partnerID=40&md5=282641e8c8de6ed4ce20aa8ce2c8adbfhttps://scholars.lib.ntu.edu.tw/handle/123456789/579939To facilitate more efficient and environmentally-friendly order picking operations, this study explores the optimal storage assignment policy in the Kiva mobile fulfilment system. First, temporal association analysis and a clustering approach are employed to identify highly correlated items to be stored in the same rack. Next, in order to avoid AGV blocking, a new turnover-rate-based decentralized storage policy (TRBDSP) is proposed. Subsequently, an order picking performance evaluation method is presented. Finally, simulation studies are performed to ascertain the effectiveness of TRBDSP. The results show that the new approach can significantly improve order picking efficiency and reduce AGV energy consumption. ? 2020cluster analysis; decentralization; efficiency measurement; energy storage; energy use; policy approach; temporal analysis; unmanned vehicle[SDGs]SDG7cluster analysis; decentralization; efficiency measurement; energy storage; energy use; policy approach; temporal analysis; unmanned vehiclejournal article10.1016/j.tre.2020.1021582-s2.0-85096663929