Fleet-level Internal Combustion Engine (ICE)-to-Electric Vehicle (EV) replacement decisions in enterprise environments remain fundamentally ill-posed due to organizational heterogeneity, multi-conflicting objectives, non-convex feasibility constraints, and structural uncertainty across economic, operational, and policy variables. Existing methodologies—either heuristic-based scoring or static optimization—fail to adapt to shifting organizational priorities and exhibit decision fragility under contextual variation. This paper presents a novel Hybrid Optimization–Simulation Framework (HOSF) comprising four integrated components: (1) a Multi-Criteria Decision Making (MCDM) scoring engine with user-configurable objective weights; (2) a Mixed-Integer Linear Programming (MILP) optimization engine enforcing 24 hard business constraints; (3) a Replacement Priority Index (RPI)—an endogenous metric derived from marginal optimization contributions; and (4) a Monte Carlo robustness layer validating decisions across 500+ stochastic scenarios. Validation on a 50-vehicle municipal fleet demonstrates 28.4% cost reduction per vehicle, 234% improvement in CO2 reduction efficiency, and 97.3% decision confidence. The framework scales to heterogeneous fleets of 500+ vehicles and enables dynamic objective reconfiguration in under 5 seconds.
Barnana et al. (Sun,) studied this question.