Paramilitary workforce planning in resource-constrained environments presents unique optimization challenges where traditional linear programming fails to accommodate conflicting operational mandates. This study introduces a lexicographic goal programming (LGP) framework, specifically calibrated for the Nigerian Security and Civil Defence Corps (NSCDC), addressing critical gaps in security sector operations research. We formulate and validate three pre-emptive GP models: multi-cadre training allocation under fiscal volatility, simultaneous armed/unarmed deployment scheduling, and weapon training time distribution under regulatory constraints. Methodologically, we integrate Hannan’s efficiency test to eliminate dominated solutions and propose a novel stochastic extension accounting for instructor absenteeism; addressing a persistent limitation in deterministic security scheduling literature. Analysis of 54 comparative studies yields a taxonomy distinguishing security-sector GP applications from civilian counterparts across ten operational dimensions. Results reveal that current weapon training regulations are stochastically infeasible at 95% reliability, while training allocation models identify a 12% fiscal buffer previously obscured by heuristic scheduling practices. Shadow price analysis demonstrates that junior cadre quotas, not budgetary limits, constitute the true binding constraint in training optimization. Beyond numerical results, we provided LINGO/GAMS code templates, extended simplex tableaux, and a four-phase change management framework for adopting quantitative methods within hierarchical paramilitary cultures. This research establishes LGP as both an optimization engine and diagnostic instrument for evidence-based security administration, offering transferable methodologies for Global South paramilitary organizations navigating expanding mandates amid fiscal austerity. The framework enables commanders to quantify trade-offs transparently, advocate for resources with analytical credibility, and align workforce planning with strategic priorities through rigorous satisficing rather than unattainable optimality.