Optimized Manpower and Location Scheduling Query for Shop Promotion Based on Records of Bicycle Sharing System

With the increasing development of big data analytics, more and more researchers have started to apply related technologies to various business projects. However, so far most of these projects have only considered the data of the shop, such as daily visitor estimation or daily sales estimation. The substantial promotion of the shop and the scheduling of promotion manpower, however, remain topics that have rarely been discussed. Therefore, this study proposes a new query to discuss these two issues simultaneously. In this new query, we conduct the following steps: (1) Using the borrowed records of the bicycle sharing system for the location of the query shop, we find out what other locations are most frequently visited by people in the vicinity of the query shop, and consider these locations as key sources of customers; (2) Using NNs to predict the traffic at these key sources; (3) Using GA to find out how to schedule the staff to distribute flyers at these key sources if the shop needs to have such arrangement. The final experimental simulation verifies the effectiveness of the proposed method.