Solving Last-Mile Logistics Problem in Spatiotemporal Crowdsourcing via Role Awareness With Adaptive Clustering

Abstract

Last-mile logistics is a crucial phase of online commodity trades. In last-mile logistics, one of the critical problems is to reasonably assign couriers to distribute the products in time in order to ensure the quality of service, especially for fresh produce. The last-mile assignment problem (LMAP) for fresh produce poses a challenge on traditional logistics since fresh produce is difficult to preserve. This article formalizes the LMAP for fresh produce via the group role assignment framework and proposes a role awareness method by using adaptive clustering in spatiotemporal crowdsourcing based on task granularity. The formalization of LMAP makes it easy to find a solution using the IBM ILOG CPLEX optimization package (CPLEX). The proposed method allows one to take the time and space factor into consideration, helps spatiotemporal crowdsourcing assign couriers for efficient delivering daily orders, and improves the quality of service in last-mile logistics. It is verified by simulation experiments. The experimental results demonstrate the practicability of the proposed solutions in this article.