Dynamic workforce allocation for train-based crowd-shipping: Multi-period optimization incorporating transit discomfort costs

Abstract

Last-mile parcel delivery faces challenges such as rising costs, labor shortages, and environmental concerns, especially in urban areas. This paper tackles these issues with a new rolling horizon framework that uses public train networks. We create a dynamic model that assigns delivery tasks to crowd-shippers (commuters) and part-time workers based on real-time data, considering train occupancy and commuter discomfort costs. Our results show that strategic delays in parcel delivery can offer economic benefits, with a delayed end-of-day approach saving 6.3% to 9.1% compared to the faster mixed-workforce approach we studied, which involved both part-timers and crowd-shippers. Additionally, we find that crowdsourced delivery is mainly cost-effective during off-peak transit hours, while part-time staff are more economical during peak times. These findings support adopting a flexible, hybrid workforce strategy that adjusts to changing transit conditions over time.