Abstract: We examine how trip duration, shaped by network structure, affects labor supply in gig-based long-haul trucking. Using data from a large trucking platform in India, we show that longer trips reduce driver reliability. Further, using a queuing-based analytical model, we find that under demand uncertainty, adaptive dynamic staffing makes the Relay Network (RN) more cost-efficient than the Point-to-Point (P2P) model. Our results highlight how the dynamics of the on-demand economy, coupled with supply behavior, shift optimal network design towards a win-win outcome.

Abstract: Adam Smith's foundational principle of division of labor has shaped organizational design for centuries, but the emergence of on-demand platforms challenges this operational paradigm in service system design. This study examines how traditional task specialization strategies perform in service systems staffed by a flexible, on-demand workforce. Using a queuing-theoretic framework, we analyze the tension between the productivity gains from specialization and the coordination and holding costs that arise when tasks are divided across multiple workers under different staffing models. We find that on-demand systems tend to favor higher levels of task division compared to traditional fixed-staffing models. We further demonstrate that the optimal level of specialization in such systems depends on market characteristics: cognitive-task markets benefit from greater specialization, while creative-task markets favor moderate specialization due to workers’ cross-functional skill sets, drawing on the concept of related variety from organizational theory.

Abstract: Supply chain resilience has become increasingly critical as global networks face frequent disruptions from various sources including natural disasters, geopolitical tensions, and pandemic-related shocks. We first introduce a stochastic relay network design model for freight transportation that incorporates supply uncertainty. A modified Benders decomposition algorithm is introduced to solve large instances efficiently. Using numerical experiments inspired by COVID-related labor disruptions, we show that RNs achieve superior throughput resilience by flexibly rerouting flows through adjacent relay points. Unlike P2P networks, which experience one-to-one throughput losses, RNs sustain performance at a 1–3% cost increase.

Abstract:We examine worker choices regarding scheduling preferences, flexibility, and compensation policies in the Indian trucking industry. Preliminary interviews reveal a preference among drivers for daily trips over a month or week long journeys, at a cost of potential wage reductions. This highlights a fundamental tension between personal preferences and financial incentives.