Behavioral modeling and operational optimization of crowdshipping systems in urban last-mile delivery logistics

Sustainable urban logistics requires doing more with what already exists. The last-mile segment is simultaneously the shortest and the most costly segment of the supply chain, the most congested and least efficient, closest to the customer and furthest from operational optimization, and the segment where growing e-commerce demand exerts the greatest pressure. Crowdshipping proposes a structurally different approach by using the mobility of occasional drivers (OD) to complement freight demand without adding vehicles. Cities generate enormous latent mobility every day, and crowdshipping explores the potential for that mobility to carry freight efficiently, sustainably, and at scale, depending on system design. Designing such a system is an operational problem that Operations Research (OR) is well suited to study. The objective of this thesis is to develop optimization models for crowdshipping platforms that progressively integrate routing decisions, incentive design, and behavioral responses from OD in order to better understand the operational and economic dynamics of hybrid delivery systems. The thesis first conducts a systematic review of the crowdshipping literature that synthesizes empirical evidence on driver behavior, platform incentives, and sustainability outcomes, while revealing gaps in existing modeling approaches. That foundation motivates a routing formulation, the Team Orienteering Problem with Occasional Drivers (TOP-OD), which places driver reward at the center of the objective and coordinates deliveries between the OD and a traditional fleet. The formulation is supported by an agile algorithm based on biased randomized heuristics, enabling high-quality solutions within operational planning horizons. Analysis of this model demonstrates that the value of crowdshipping is not uniform across urban environments but depends critically on the spatial distribution of demand, a finding that naturally motivates a bi-objective exploration of the trade-off between reward maximization and cost minimization. The resulting Pareto frontier reveals that the relationship between incentives and efficiency is asymmetric and context-dependent, providing logistics operators with a structured foundation for incentive design. The thesis further develops an integrated framework in which routing and compensation are determined simultaneously and driver participation is modeled as a probabilistic response to the incentives offered. This framework is formulated under both deterministic and stochastic travel-time conditions and solved exactly to establish rigorous benchmarks for a problem that had not previously been addressed in a fully integrated manner. Understanding how crowdshipping works is ultimately about understanding how cities move, how people make decisions under uncertainty, and how platforms can be designed to align individual incentives with collective efficiency. These are not purely technical questions. They lie at the intersection of OR, behavioral economics, and urban policy, and their answers have consequences that extend well beyond the delivery of parcels.