Aiming at the problems of delayed delivery and low transportation efficiency arising from random delays at each task node in the express delivery service process, this paper developed a Petri net model for express delivery processes to improve delivery timeliness and operational efficiency. Simulation experiments were conducted using CPN Tools, and a correlation matrix analysis was adopted to diagnose process bottlenecks and identify the key root causes of delays. Three optimization strategies were accordingly proposed: parallel processing of parcel sorting and collection, advance scheduling of vehicle deployment tasks, and consolidation of redundant inspection links. This study organically integrated timed Petri net modeling, simulation, and incidence matrix analysis to accurately locate and eliminate abnormal process links. Process structural optimization was achieved while conforming to real-world scenarios with stochastic delays. A reusable process optimization framework was thus established for complex logistics operations. Quantitative verification demonstrates that the optimized model achieves an over 20% higher task execution success rate compared with the original model. The proposed framework can support cost reduction and efficiency improvement for express delivery operations, and deliver feasible ideas and methodologies for the optimization of analogous logistics service processes.