Multicriteria Route Optimization for Bicycle Delivery Drivers

Abstract: The rapid growth of the gig economy has led to an increasing number of bicycle delivery drivers, who face challenges such as time constraints, physical and traffic stress, and weather conditions. Current routing models focus on operational efficiency but often overlook critical human factors. This study aims to address these gaps by developing a multicriteria route optimization model specifically for bicycle delivery drivers, integrating factors such as delivery time windows, road stress, and weather impacts.

Building upon last-mile logistics frameworks, we extend their application to bike delivery networks. Our model leverages multi-objective optimization techniques to balance the competing delivery performance criteria, driver well-being, and safety. Unlike existing methods focusing solely on operational costs or efficiency, our approach incorporates human-centered criteria, addressing the need for sustainable and fair urban mobility solutions.

Using real-world data on traffic patterns, weather conditions, and delivery schedules, we will simulate delivery scenarios in San Francisco’s urban bike network to assess the model's effectiveness under realistic conditions. This research can potentially improve urban delivery logistics by facilitating real-time route optimization through mobile platforms, ensuring a balance between operational efficiency, sustainability, and driver safety.

The insights gained from this case study could inform future work on urban mobility and delivery services, offering guidance for adapting similar models to other urban settings.