Multi-Shift Scheduling of Electric Service Operations Under Fuzzy Uncertainty via Preference-Guided Deep Learning: The Single-Vehicle Case

The electrification of field service fleets introduces complex constraints: shift limits, overtime fairness, and battery–range feasibility. This paper proposes the Multi-Shift Single Electric Vehicle Routing Problem under Possibilistic Uncertainty (MS-SEVRP-PU), a formulation focused on a single-vehicle multi-shift planning unit and capturing imprecise travel/service times and state-of-charge dynamics. Travel durations and energy consumption are modelled as triangular fuzzy numbers to reflect expert knowledge when probabilistic data is limited. A closed-form credibility function evaluates overtime risk, while an Ordered Weighted Averaging (OWA) aggregation of per-shift risks ensures fairness by discouraging systematic overload on specific shifts. To solve this multi-objective problem, we develop a Pareto-Conditioned Transformer with risk-aware and battery-conscious large neighbourhood search (PCT-RABLNS), combining a preference-conditioned attention policy with targeted local search. Computational experiments on calibrated municipal maintenance case studies indicate that PCT-RABLNS improves hypervolume by 2–5% over strong baselines and reduces maximum shift overtime risk by 15–25%, with a marginal makespan overhead of only 1–3%. The results demonstrate that the proposed framework is a promising decision-support approach for energy-aware, risk-fair, and operationally compliant planning of single-vehicle, multi-shift electric service operations, jointly integrating multi-shift routing, fuzzy uncertainty, and preference-conditioned reinforcement learning. The paper also discusses how the framework can be extended to multi-vehicle settings.