随机扰动环境下数控加工工艺规划与车间调度集成的能效机理及其优化问题研究

In the practical machining system, a series of random interruptions including random arrival of new parts and random machine break down would occur subsequently. The Energy consumption characteristics of machining system under such interruptions conditions tends to be quite complicated, varying substantially with respect to multiple dynamic processes. Considering that the effects between process planning, job shop scheduling and energy efficiency, this project attempts to investigated the interaction effects between integration of process planning and scheduling and energy efficiency for the random interruption triggered machining system, and presents a set of theory and methods for energy-efficient integration of process planning and scheduling. Firstly, the energy consumption characteristics of machining system under random interruptions is analyzed and the influence factors are determined. Secondly, the influence effect model of integration of process planning and scheduling on energy efficiency for the uncertain machining system is developed and the interaction effects of cutting parameters, process routes and scheduling is revealed. Thirdly, the reactive integration model of process planning and scheduling model is developed to maximize the energy efficiency for machining system under interruption. The estimation of distribution algorithm combined with Q-learning is present as an optimization solution for the reactive integration model. Furthermore, a robust integration model is developed for the interruption triggered machining system and a decomposition and interactive optimal searching algorithm is presented to obtained the robust integration schemes. Finally, a practical machining workshop is selected for experimental validation. This research project will form a set of energy-efficient integration method of process planning and scheduling for machining considering multiple interruptions, which is of significance for providing technical support of energy efficiency optimization and improvement for mechanical machining system.

针对随机扰动环境下数控加工系统能量流程复杂、能效动态变化大，且工艺规划与车间调度集成的能效作用机理复杂不明确等问题，将研究随机扰动环境下数控加工工艺规划与车间调度集成的能效机理及其优化问题。将揭示随机扰动环境下数控加工系统能效特性与能效影响因素，研究随机扰动环境下工艺规划与调度集成的能效映射关系模型与作用机理；建立响应实时扰动的工艺规划与车间调度高能效集成优化模型，提出基于多种群分布估计算法与Q-learning的动态集成优化方法，实现对随机扰动事件的动态实时响应；以数控加工车间随机事件历史数据为基础，研究随机扰动环境下工艺规划与车间调度高能效集成鲁棒优化模型与方法。最后，选取实际数控加工车间开展实验验证与应用研究。本项目属于绿色制造领域应用基础研究，密切结合机械制造业节能减排产业发展需求，将形成一套面向随机扰动事件的数控加工能效优化理论与方法，对提高数控加工系统能量效率具有重要的意义。

本项目聚焦数控加工系统能效优化问题，针对新工件随机到达、机床故障、紧急工件插单等扰动事件，以工艺规划与车间调度集成优化为切入点，研究随机扰动环境下数控加工系统能效优化理论与方法。揭示了随机扰动环境下数控加工系统多源能量流特性，提出了基于工业物联网（IoT）和数据驱动的制造车间多层级能效在线采集方法，为能效映射关系模型和作用机理研究提供了基础数据支持；集成工艺路线、工艺参数、车间调度等多重离散-连续混合变量，建立了面向随机扰动的工艺规划与车间调度集成的能效映射关系模型，揭示了数控加工工艺规划与车间调度集成的能效作用机理；提出了面向随机扰动的工艺路线、工艺参数与车间调度动态集成优化机制，建立了面向单一扰动和面向双重扰动的数控加工工艺规划与车间调度动态集成优化模型；结合优化问题特征，设计了多种高效的启发式算法进行求解，并对所提出算法进行了仿真测试以验证其有效性；选取以实际加工车间为应用场景，对所提出动态集成优化方法的节能效果进行了验证分析。本项目研究成果对于提升数控加工系统能量效率具有重要的意义，可有效助力我国制造业绿色可持续发展需求。

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