SkillChain-Gym: A Benchmark for Reskilling-Aware Production-Inventory Control under Disruptions

SkillChain-Gym：面向中断环境下技能重塑感知生产库存控制的基准测试

Abstract: Production planning increasingly has to treat workforce capability as a decision variable: certifications lapse when skills are not maintained, new products require skills the current workforce does not hold, and reskilling competes for the same worker hours needed for production. Existing operations benchmarks usually treat labor as exogenous, while workforce-planning models with skills and learning are rarely released as reusable testbeds.

摘要： 生产计划越来越需要将劳动力能力视为一个决策变量：当技能未得到维护时，认证会失效；新产品需要现有劳动力所不具备的技能；而技能重塑（reskilling）会占用生产所需的工时。现有的运营基准测试通常将劳动力视为外生变量，而包含技能和学习过程的劳动力规划模型很少作为可复用的测试平台发布。

We introduce SkillChain-Gym, a benchmark specification for reskilling-aware production-inventory control: a single-site environment with stylized worker skill-state dynamics, hard threshold certification, forgetting, and capacity-consuming training actions constrained by the same per-worker time budget as production. The benchmark includes seed-controlled disruption scenarios, three feasibility modes with projection diagnostics, deterministic replay, and metrics covering operations, resilience, capability growth, and training-access distribution.

我们引入了 SkillChain-Gym，这是一个用于技能重塑感知生产库存控制的基准规范。它提供了一个单站点环境，包含程式化的员工技能状态动态、硬阈值认证、遗忘机制，以及与生产共享每位员工工时预算的资源消耗型培训动作。该基准测试包含种子控制的中断场景、三种带有预测诊断的可行性模式、确定性重放，以及涵盖运营、韧性、能力增长和培训获取分布的评估指标。

We evaluate production-only, reactive adaptive, water-filling adaptive, and static-insurance policies with budget variants over 60-shift horizons with paired statistical tests. The results are regime-dependent rather than a ranking. Training-capable policies dominate the production-only baseline, and maintenance training is necessary under forgetting even without disruptions.

我们通过配对统计检验，在 60 个班次的周期内，对仅生产策略、反应式自适应策略、注水式（water-filling）自适应策略以及带有预算变体的静态保险策略进行了评估。结果显示，表现优劣取决于具体情境，而非单一的排名。具备培训能力的策略优于仅生产的基准策略；即使在没有中断的情况下，考虑到技能遗忘，维护性培训也是必要的。

Among training-capable classes, adaptive training helps when bottlenecks are visible in the forecast, while a lean static cross-training plan, a deliberately favorable comparator whose structure encodes relevant skill contingencies, acts as strong insurance under surprise shocks and absenteeism. Capacity slack and the forgetting rate govern the boundary between these regimes. No policy class dominates across regimes, motivating forecast-driven controllers that decide when to buy skill insurance and when to react.

在具备培训能力的策略类别中，当预测中出现明显的瓶颈时，自适应培训非常有效；而精益的静态交叉培训计划（一种刻意设计的有利对比方案，其结构编码了相关的技能应急预案）则在面对突发冲击和缺勤时提供了强有力的保险。产能冗余和遗忘率决定了这些策略适用情境的边界。没有任何一种策略能在所有情境下占据主导地位，这促使我们需要开发基于预测的控制器，以决定何时购买“技能保险”以及何时采取反应式行动。