In Production Scheduling, the Flexible Job Shop Scheduling Problem (FJSSP) aims to optimize a sequence of operations and assign each to an eligible machine with varying processing times. For integration of the workforce, each machine also requires a worker to be present to process an operation which additionally affects the processing times. The resulting problem is called Flexible Job Shop Scheduling Problem with Worker Flexibility (FJSSP-W). The FJSSP has been approached with various problem representations, including Mixed Integer Linear Programming (MILP), Constrained Programming (CP), and Simulation-based Optimization (SBO). In the latter area in particular, there exists a large number of specialized Evolutionary Algorithms (EA) like Particle Swarm Optimization (PSO) or Genetic Algorithms (GA). Yet, the solvers are often developed for single use cases only, and validated on a few selected test instances, let alone compared with results from solvers using other problem representations. While suitable approaches do also exist, the design of the FJSSP-W instances is not standardized and the algorithms are hardly comparable. This calls for a systematic benchmarking environment that provides a comprehensive set of FJSSP(-W) instances and supports targeted algorithm development. It will facilitate the comparison of algorithmic performance in the face of different problem characteristics. The present paper presents a collection of 402 commonly accepted FJSSP instances and proposes an approach to extend these with worker flexibility. In addition, we present a detailed procedure for the evaluation of scheduling algorithms on these problem sets and provide suitable model representations for this purpose. We provide complexity characteristics for all presented instances as well as baseline results of common commercial solvers to facilitate the validation of new algorithmic developments.

翻译：在生产调度中，柔性作业车间调度问题（FJSSP）旨在优化工序序列，并将每道工序分配给一台符合条件的机器，且各机器加工时间不同。为整合劳动力因素，每台机器还需配备一名工人方可执行工序，这进一步影响了加工时间。由此产生的问题称为带工人灵活性的柔性作业车间调度问题（FJSSP-W）。针对FJSSP已发展出多种问题表示方法，包括混合整数线性规划（MILP）、约束规划（CP）和基于仿真的优化（SBO）。特别是在后一领域，存在大量专门的进化算法（EA），如粒子群优化（PSO）或遗传算法（GA）。然而，这些求解器通常仅为单一用例开发，并在少数选定测试实例上进行验证，更遑论与采用其他问题表示方法的求解器结果进行比较。尽管也存在合适的方法，但FJSSP-W实例的设计缺乏标准化，算法之间难以直接比较。这迫切需要一种系统化的基准测试环境，能够提供全面的FJSSP（-W）实例集并支持针对性算法开发。该环境将有助于在不同问题特征下比较算法性能。本文收集了402个公认的FJSSP实例，并提出了一种为其扩展工人灵活性的方法。此外，我们详细阐述了在这些问题集上评估调度算法的流程，并为此提供了合适的模型表示形式。我们提供了所有实例的复杂度特征以及常用商业求解器的基准结果，以促进新算法开发的验证工作。