Data-driven storage location method for put system in Chinese flower auction centres

The rapid increase in daily transactions poses severe challenges for Chinese flower auction centres, including more frequent travels for distribution workers and longer waiting times for buyers. Two distinctive features of Chinese flower auctions further complicate the studied process: buyer identities and purchased volumes of present buyers are not known in advance. Buyer identities become known only after their first bid and purchased volumes by present buyers remain uncertain until the end of the auction. To address these problems, we propose a data-driven storage location method for put systems in Chinese flower auction centres which reserves predetermined locations near the distribution I/O point to large potential buyers before their actual arrival and the remaining locations to other arriving buyers according to the closest open location policy. We use the mesh adaptive direct search algorithm to determine the size of the reserved area and release time of any unoccupied locations to later arriving buyers. The proposed method is verified via a case study, and results show that it outperforms the existing method by a fairly large margin.