In this work we address the challenge of integrating production planning and maintenance optimization for a process plant. We consider uncertain predictions of the equipment degradation by adopting a stochastic programming formulation with decision-dependent uncertainty. The probability of the uncertain parameters, in this work the remaining useful time of the plant, depends on the operating conditions of the plant which is modeled by embedding a prognosis model, the Cox model, into the optimization problem. A separation of the variables is suggested to decompose the MINLP formulation via two different primal decomposition algorithms. We provide computational results and compare the performance of the proposed decompositions with the global solver BARON enhanced with a custom branching priority strategy.

在这项工作中，我们解决了整合过程工厂的生产计划和维护优化的挑战。我们通过采用具有决策相关不确定性的随机规划公式来考虑设备退化的不确定预测。不确定参数的概率，在这项工作中，工厂的剩余可用时间，取决于工厂的运行条件，通过将预测模型 Cox 模型嵌入到优化问题中来建模。建议分离变量以通过两种不同的原始分解算法分解 MINLP 公式。我们提供了计算结果，并将所提出的分解与使用自定义分支优先级策略增强的全局求解器 BARON 的性能进行了比较。