Scheduling the maintenance based on the condition, respectively the degradation level of the system leads to improved system's reliability while minimizing the maintenance cost. Since the degradation level changes dynamically during the system's operation, we face a dynamic maintenance scheduling problem. In this paper, we address the dynamic maintenance scheduling of manufacturing systems based on their degradation level. The manufacturing system consists of several units with a defined capacity and an individual dynamic degradation model, seeking to optimize their reward. The units sell their production capacity, while maintaining the systems based on the degradation state to prevent failures. The manufacturing units are jointly responsible for fulfilling the demand of the system. This induces a coupling constraint among the agents. Hence, we face a large-scale mixed-integer dynamic maintenance scheduling problem. In order to handle the dynamic model of the system and large-scale optimization, we propose a distributed algorithm using model predictive control (MPC) and Benders decomposition method. In the proposed algorithm, first, the master problem obtains the maintenance scheduling for all the agents, and then based on this data, the agents obtain their optimal production using the distributed MPC method which employs the dual decomposition approach to tackle the coupling constraints among the agents. The effectiveness of the proposed method is investigated on a case study.
翻译:由于系统运行期间的降解水平动态变化,我们面临一个动态的维护进度安排问题。在本文件中,我们处理的是基于其降解水平的制造系统动态维护时间安排问题。制造系统由几个功能确定并采用个人动态降解模型的单位组成,以优化其奖励。这些单位出售其生产能力,同时维持基于降解状态的系统以防止失败。制造单位共同负责满足系统的需求。这在代理商之间造成一种混合整数动态维护进度安排问题。因此,我们面临一个大规模混合整数动态维护进度安排问题。为了处理系统的动态模型和大规模优化,我们建议采用模型预测控制(MPC)和Benders脱形法的分布算法。首先,总问题获得所有代理商的维护进度安排,然后根据这些数据,代理商利用分布式的MPC方法获得最佳生产。采用双重拆分法的方法,采用双重拆分法的方法调查了双重拆分方法,以克服组合代理人的制约。