Ant Colony Algorithm Based on Total Logistics Delivery Efficiency Metrics for Crowdsourced On-Demand Delivery

Crowdsourced on-demand delivery is being required by many industries with the increasing demand of online shopping and on-demand food. However, it is difficult to ensure the delivery efficiency due to the uncertainty of delivery time for crowdsourced couriers in total delivery process. An ant colony algorithm based on total logistics delivery efficiency metrics is proposed in order to ensure the efficiency of crowdsourced on-demand delivery. Firstly, two metrics are designed to describe the total logistics delivery efficiency quantitatively. The average delivery time of each order and the number of orders completed on time are calculated in total logistics delivery efficiency metrics. Secondly, a total logistics crowdsourced on-demand delivery model (TLCODM) is built considering the transport efficiency and order punctuality. This model serves as the fundamental support for crowdsourced on-demand delivery. Finally, an ant colony algorithm based on total logistics delivery efficiency metrics (ACO-TLDE) is proposed to obtain the optimal scheduling solutions. The total logistics delivery efficiency metrics are used to guide the update of ant colony algorithm. The effectiveness of TLCODM and ACO-TLDE are verified by experiments.