Configuration-Driven Machine Availability Constraints in Job Shop Scheduling

This dataset supports research on configuration-driven job shop scheduling with machine availability constraints, where machine reliability is directly affected by operating configurations. In real-world manufacturing systems—particularly Reconfigurable Manufacturing Systems (RMS)—factors such as tooling, spindle speed, and load level influence equipment wear, degradation, and maintenance needs. The dataset captures this configuration–reliability relationship by providing: Operation-specific processing times across multiple machine configurations Setup times between configurations Configuration eligibility per operation Machine-specific maintenance windows Configuration-induced degradation effects on machines It is designed for use in optimization models that jointly schedule production and maintenance, enabling more responsive and cost-efficient decisions in systems where machine reliability varies with usage. The structure is compatible with MILP formulations that integrate degradation-aware maintenance planning in RMS environments.本数据集支持面向机器可用性约束的配置驱动型车间调度研究，其中机器可靠性直接受运行配置影响。在实际制造系统中，尤其是可重构制造系统（Reconfigurable Manufacturing Systems, RMS）内，工装配置、主轴转速与负载水平等因素会影响设备磨损、性能退化及维护需求。 本数据集通过以下内容刻画该配置与可靠性之间的关联关系： 1. 多机器配置场景下的工序专属加工时长 2. 不同配置间的换装时长 3. 各工序可选用的机器配置规则 4. 各机器的专属维护时段 5. 配置引发的机器性能退化影响 本数据集专为联合调度生产与维护的优化模型设计，可在机器可靠性随使用状态动态变化的系统中，助力实现更具响应性且成本效益更优的决策。该数据集的结构兼容在可重构制造系统环境中集成了考虑退化的维护规划的混合整数线性规划（Mixed Integer Linear Programming, MILP）模型。