[2606.17269] Skill-Constrained Model Predictive Control for Resilient Manufacturing Supply Chains
[Submitted on 15 Jun 2026]
Title:Skill-Constrained Model Predictive Control for Resilient Manufacturing Supply Chains
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Abstract: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.
Subjects:
Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
Cite as: arXiv:2606.17269 [cs.AI]
(or arXiv:2606.17269v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.17269
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Carlos Eduardo Sanoja [view email] [v1] Mon, 15 Jun 2026 20:23:12 UTC (95 KB)
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