The development of delivery optimization algorithms for real-business applications of immediate and planned delivery

Abstract

Due to the expansion of the E-commerce market, the impact of the COVID-19 infection, and the lack of delivery resources caused by the recent labor shortage, the importance of the home delivery business and the need to improve delivery efficiency are increasing in various industries. In particular, the last-one mile, which is the part of delivery from the final location such as a store to the consumers, is facing the need to consider a variety of new requirements and constraints due to the increasing demand for immediate delivery (Q-commerce) in addition to conventional E-commerce delivery. In this study, we develop a delivery optimization algorithms for both "immediate delivery" and "planned delivery" as part of system construction for a delivery platform to address the above-mentioned issues. Specifically, we developed a driver matching optimization algorithm for immediate delivery and a route optimization algorithm for planned delivery. In both cases, we comprehensively took into account the optimization requirements and constraints necessary to solve problems in practical operations, and conducted comparisons and experiments of appropriate algorithms and models. We were able to observe a constant rate of improvement in various evaluation indices by applying the algorithms to actual business operations.