Energy-aware integrated order assignment and routing for heterogeneous couriers in on-demand delivery

On-demand delivery (ODD) platforms increasingly rely on heterogeneous courier workforces (in-house, supplementary dedicated, and crowdsourced couriers) to meet stringent time-window requirements. Among these types, crowdsourced couriers -the workforce backbone -predominantly utilize battery-swapping two-wheeled electric vehicles (TWEVs) to sustain high-frequency operations. In the face of these evolving characteristics, however, real-world operational practices reveal a critical gap: current dispatching systems often manage in-house couriers, supplementary dedicated couriers, and crowdsourced couriers through separate decision processes rather than a unified system-level mechanism, while also overlooking energy-logistics couplings, including energy-constrained order assignments, speed variations dependent on state of charge (SoC) and mandatory detours for battery swapping. Such fragmented dispatching practices and omitted energy considerations frequently lead to unexecutable plans and delivery delays. To address this, we propose an energy-aware integrated order assignment and routing model that unifies the dispatching of heterogeneous courier workforces by explicitly incorporating SoC-dependent travel speeds and battery swapping detours. To solve this computationally challenging problem, we design a nested tabu search algorithm based on a bi-level “fast search-precise evaluation” structure. The outer level conducts fast global assignment search with an incremental routing heuristic, while the inner level incrementally solves courier-level mixed-integer programming (MIP)-based routing subproblems to ensure accurate cost evaluation. Extensive experiments using real-world data demonstrate that our algorithm yields high-quality solutions efficiently, accelerating computation by 2–3 orders of magnitude over Gurobi with a 1.6% average gap, whereas traditional tabu search and adaptive large neighborhood search (ALNS) exhibit substantially larger average gaps of over 12%. Crucially, the proposed framework significantly outperforms conventional dispatching strategies that neglect energy-related constraints. Furthermore, sensitivity analyses provide managerial insights into how increasing swap-cabinet availability, expanding crowdsourced capacity, improving battery efficiency and capacity, and adjusting low-battery thresholds can enhance system efficiency. Overall, this study paves the way for sustainable and reliable urban delivery systems.