This paper addresses the Flexible Job Shop Scheduling Problem and its extension with Worker Flexibility, which integrates workforce assignment into machine-operation scheduling. Diverse solvers have been proposed across multiple optimization domains including Mathematical Programming, Constraint Programming, and Simulation-Based Optimization, or Simulation-based Optimization. These are often tailored to narrow use cases and validated on limited test problem sets, hindering cross-domain comparison. To overcome this, a comprehensive benchmarking environment built on 402 standardized Flexible Job Shop Scheduling Problem instances is introduced and systematically extended to include worker flexibility. This creates a hitherto unique collection of ready-to-use worker flexibility instances. The benchmark suite features several metrics for algorithm performance assessment, the visualization of algorithmic results, as well as state-of-the-art baseline results. This enables rigorous, reproducible, and comparable performance analysis between solvers and scheduling problem subdomains. Through the simulation-based integration of uncertainties in processing times as well as resource availabilities, the environment supports the development and evaluation of robust optimization strategies. The present work lays a foundation for targeted algorithm development and consistent performance evaluation in production scheduling research.

翻译：本文针对柔性作业车间调度问题及其扩展——具备工人柔性的问题（即将劳动力分配集成到机器-作业调度中）展开研究。现有求解器涵盖数学规划、约束规划和基于仿真的优化等多个优化领域，但这些方法通常针对狭窄用例设计，并在有限测试问题集上验证，阻碍了跨领域比较。为克服这一局限，我们基于402个标准化柔性作业车间调度问题实例构建了全面的基准测试环境，并系统性地将其扩展至包含工人柔性，形成了迄今唯一的可直接使用的工人柔性实例集。该基准套件提供多项算法性能评估指标、算法结果可视化工具以及现有最优基线结果，支持对求解器及调度问题子域进行严格、可复现且可比的性能分析。通过基于仿真的加工时间与资源可用性不确定性集成，该环境支持鲁棒优化策略的开发与评估。本研究为生产调度领域的定向算法开发与标准化性能评估奠定了基础。