Simulation-Optimization for Resource Allocation at SF Express Hub

Parcel-sorting hubs at SF Express face growing pressure to allocate resources—including labor, equipment, and workstations—efficiently amid rising volume volatility and tighter fulfillment windows. Traditional spreadsheet-based planning methods have struggled to keep pace, resulting in frequent mismatches between resource supply and operational demand. This study introduces a simulation-optimization framework implemented at the Shenzhen hub to address these challenges. A discrete-event simulation model captures operational variability and interdependencies, and an embedded optimization solver identifies cost-effective resource plans under real-world constraints. A three-phase field test conducted from March to April 2024 on a high-priority ground-to-air operation achieved an 18.7% cost reduction through simulation-guided refinement and a best case 33.5% savings using solver-based optimization. When scaled across all Shenzhen hub operations for the remainder of 2024, the framework delivered an average cost reduction of 11% with the largest gains observed in air-bound flows constrained by outbound scheduling. Designed for fast deployment by frontline teams, the framework enables timely data-driven decisions without requiring advanced analytical expertise. This work offers a scalable, field-tested approach for improving resource allocation in dynamic logistics environments by combining analytical rigor with operational usability.

History: This paper was refereed.