aceDecision intelligence for electric bus charging scheduling through LLM-driven reward shaping and reinforcement learning
The rapid expansion of electric bus fleets has made charging scheduling a critical decision-intelligence problem in urban fleet-energy logistics. Reinforcement learning (RL) is promising for this problem, but reward function design still relies on manual trial-and-error and transfers poorly across scenarios. This paper develops an artificial intelligence and operations research (AI-OR) decision intelligence framework that integrates large language model (LLM)-driven reward shaping with RL-based sequential decisi…
An LLM-driven reward iteration framework for reinforcement learning scheduled electric bus charging at lower cost than traditional methods across synthetic and real-world Guangzhou bus station scenarios.
The reported savings are nominal within-day charging costs, and the lower 22.5 % figure relies on an approximate adjustment for energy left on board.
Evidence
- Peer-reviewedTransportation Research Part C: Emerging Technologies2026-10-21
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Truvace Impact Record TRV-2026-2177, v1: “Decision intelligence for electric bus charging scheduling through LLM-driven reward shaping and reinforcement learning.” Truvace, 2026-10-22. /record/TRV-2026-2177 (accessed at citation time). sha256 0e181642d5da91c1…
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