Abstract Dynamic flexible job shop scheduling problem is studied under the events such as new order arrivals, changes in due dates, machine breakdowns, order cancellations, and appearance of urgent orders. This paper presents a constructive algorithm which can solve FJSP and DFJSP with machine capacity constraints and sequence-dependent setup times, and employs greedy randomized adaptive search procedure (GRASP). Besides, Order Review Release (ORR) mechanism and order acceptance/rejection decisions are also incorporated into the proposed method in order to adjust capacity execution considering customer due date requirements. The lexicographic method is utilized to assess the objectives: schedule instability, makespan, mean tardiness and mean flow time. A group of experiments is also carried out in order to verify the suitability of the GRASP in solving the flexible job shop scheduling problem. Benchmark problems are formed for different problem scales with dynamic events. The event-driven rescheduling strategy is also compared with periodical rescheduling strategy. Results of the extensive computational experiment presents that proposed approach is very effective and can provide reasonable schedules under event-driven and periodic scheduling scenarios.

中文翻译：

---

动态灵活作业车间调度的贪婪随机自适应搜索

摘要 研究了新订单到达、到期日变更、机器故障、订单取消、紧急订单出现等事件下的动态柔性作业车间调度问题。本文提出了一种构造算法，该算法可以解决具有机器容量约束和依赖于序列的设置时间的 FJSP 和 DFJSP，并采用贪婪随机自适应搜索程序 (GRASP)。此外，订单审查发布（ORR）机制和订单接受/拒绝决策也被纳入建议的方法中，以根据客户到期日要求调整产能执行。字典法用于评估目标：进度不稳定性、完工时间、平均迟到和平均流动时间。还进行了一组实验，以验证 GRASP 在解决柔性作业车间调度问题中的适用性。基准问题是针对具有动态事件的不同问题规模形成的。事件驱动的重调度策略也与周期性重调度策略进行了比较。大量计算实验的结果表明，所提出的方法非常有效，可以在事件驱动和周期性调度场景下提供合理的调度。