Nowadays, an efficient and robust plan for maintenance activities can reduce the total cost significantly in the equipment-driven industry. Maintenance activities are directly associated with the impact on the plant output, production quality, production cost, safety, and the environmental performance. To address this challenge more broadly, this paper presents an optimization model for the problem of flexible flowshop scheduling in a series-parallel waste-to-energy (WTE) system. To this end, a preventive maintenance (PM) policy is proposed to find an optimal sequence for processing tasks and minimize the delays. To deal with the uncertainty of the flexible flowshop scheduling of waste-to-energy in practice, the work processing time is modeled to be uncertain in this study. Therefore, a robust optimization model is applied to address the proposed problem. Due to the computational complexity of this model, a novel scenario-based genetic algorithm is proposed to solve it. The applicability of this research is shown by a real-life case study for a WTE system in Iran. The proposed algorithm is compared against an exact optimization method and a canonical genetic algorithm. The findings confirm a competitive performance of the proposed method in terms of time savings that will ultimately save the cost of the proposed PM policy.

如今，有效而稳健的维护活动计划可以显着降低设备驱动行业的总成本。维护活动直接关系到对工厂产量、生产质量、生产成本、安全和环境绩效的影响。为了更广泛地应对这一挑战，本文提出了一种针对串并联垃圾发电 (WTE) 系统中灵活流水车间调度问题的优化模型。为此，提出了一种预防性维护 (PM) 策略，以找到处理任务的最佳顺序并最大限度地减少延迟。为应对实践中变废为能柔性流水车间调度的不确定性，本研究将工作处理时间建模为不确定性。所以，一个稳健的优化模型被应用于解决所提出的问题。由于该模型的计算复杂性，提出了一种新的基于场景的遗传算法来解决它。伊朗垃圾焚烧发电系统的真实案例研究表明了这项研究的适用性。将所提出的算法与精确优化方法和规范遗传算法进行比较。研究结果证实了所提出的方法在节省时间方面的竞争性能，这将最终节省所提出的 PM 政策的成本。将所提出的算法与精确优化方法和规范遗传算法进行比较。研究结果证实了所提出的方法在节省时间方面的竞争性能，这将最终节省所提出的 PM 政策的成本。将所提出的算法与精确优化方法和规范遗传算法进行比较。研究结果证实了所提出的方法在节省时间方面的竞争性能，这将最终节省所提出的 PM 政策的成本。