E-cargo bike route optimization with rider fatigue considerations: A chance-constrained programming approach

Electric cargo (e-cargo) bikes offer a promising, sustainable alternative for last-mile logistics. However, rider fatigue remains a critical, yet often overlooked, constraint, impacting both operational performance and rider well-being. This study introduces and formulates a Chance-Constrained Heterogeneous and Multi-Trip Vehicle Routing Problem (CC-HMVRP) that accounts for rider fatigue, incorporating load mass, environmental conditions, and rider characteristics into delivery planning. A mixed-integer linear programming (MILP) formulation and a modified adaptive large neighborhood search (ALNS) solution method are proposed to handle larger instances. We examine how wind speed and temperature influence battery and rider energy levels. Numerical results show a 27.2% reduction in total energy consumption compared to deterministic models, while riders retain 62.8% more available time before reaching fatigue. These findings enhance sustainability, improve efficiency, and support rider well-being, offering new insights for optimizing last-mile delivery operations under real-world constraints. • Introduces a chance-constrained programming model for e-cargo bike routing with rider fatigue limits. • Develops an Adaptive Large Neighborhood Search (ALNS) algorithm for large-scale optimization. • Demonstrates a 27.2% reduction in total energy consumption while improving rider endurance.