In skill-constrained production-inventory systems, the qualified human capacity available tomorrow depends on training decisions made today: production requires certified workers, certifications decay unless maintained, and training consumes the same scarce worker hours that production needs now. We study a closed-loop skill-constrained model predictive controller that, at every shift, solves a finite-horizon mixed-integer program over production, inventory, backlog, and training, with binary predicted certification, hard production eligibility, and an interpretable terminal value that prices certified-capacity gaps at the horizon boundary; only the first-period action is applied before replanning. On synthetic, seed-controlled SkillChain-Gym scenarios - announced and surprise new-skill shocks, demand shocks, absenteeism, forecast- and availability-quality modes, capacity-boundary and training-rate sweeps, and negative controls - we evaluate the controller against production-only and maintenance-only ablations, static cross-training insurance plans, and a strong reactive heuristic, under an ex-ante locked configuration and paired statistics. The result is regime dependence, not superiority: no policy class dominates. Predictive control helps when skill or labor bottlenecks are forecastable early enough for training to complete; lean static insurance remains hard to beat under surprise shocks, near the demand-capacity boundary, and wherever pre-shock slack makes insurance cheap. Attribution ablations separate certification maintenance, re-acquisition of lapsed certifications, and greenfield skill acquisition. Forecastability, not adaptivity per se, decides when predictive control pays.

翻译：在技能约束的生产-库存系统中，未来可用的合格人力容量取决于当前的培训决策：生产需要认证工人，认证若未维护将失效，而培训消耗的是当前生产同样亟需的稀缺工时。我们研究一种闭环技能约束模型预测控制器，该控制器在每个班次中求解一个有限时域混合整数规划，涵盖生产、库存、积压与培训，并包含二进制预测认证状态、硬性生产资格约束以及一个可解释的终值函数，该函数在时域边界处对认证能力缺口进行定价；仅执行第一期动作后重新规划。在基于种子控制的合成SkillChain-Gym场景中——包括公告性与突发性新技能冲击、需求冲击、缺勤、预测与可用质量模式、容量边界与培训率扫描以及阴性对照组——我们以事前锁定配置与配对统计方式，将控制器的性能与仅生产消融实验、仅维护消融实验、静态交叉培训保险策略以及强反应式启发式方法进行对比。结果呈现的是情景依赖性而非优劣性：没有策略类别占据主导地位。当技能或劳动力瓶颈可被提前足够早地预测以完成培训时，预测控制发挥作用；而在突发性冲击、接近需求-容量边界处以及冲击前松弛使得保险成本低廉的情境下，精简静态保险仍难以被超越。归因消融实验分别考察了认证维护、失效认证重新获取以及全新技能获取。决定预测控制价值的因素在于可预测性本身，而非适应性。