Equity-Driven Workload Allocation for Crowdsourced Last-Mile Delivery

Abstract

Crowdshipping, a rapidly growing approach in Last-Mile Delivery (LMD), relies on independent crowdworkers to fulfill delivery orders. Building a sustainable network of crowdshippers is crucial for the long-term success of such systems, as participation is primarily driven by fair compensation. This is especially important for workers who rely on crowdwork as their main source of income, making equitable pay not just a matter of fairness but of financial well-being. In this study, we address several key questions that gig-economy platforms concerned with fair pay may ask: How can equity be measured? What are the associated cost implications? And how can potential drawbacks be managed? Our main contribution is the development of a practical, equity-oriented framework tailored to crowdshipping within an LMD environment. Inspired by the real-world operations of several crowdshipping platforms, the framework operates in real time and is built around a bi-objective optimization model that balances equity and cost. This allows us to systematically explore trade-offs and identify the equity measures that most effectively capture this balance. We demonstrate that even a modest reduction in cost efficiency (e.g., 2.5%) can lead to substantial improvements in equity; potentially up to 65%. Our results provide actionable insights for practitioners, including guidance on selecting appropriate equity measures. We also find that the best equity outcomes occur when the crowdshipper pool is kept relatively small. Furthermore, we quantify the performance loss of high- and low-performing crowdshippers as the pool size increases, offering valuable insights for workforce planning and management. Along similar lines, we demonstrate that our framework remains effective in managing vehicle shortages in dynamic environments while achieving comparable levels of equity improvement.