针对MTO(Make To Order)生产模式下,设备故障带来的拖期交货和产品质量下降等问题,建立了以最小化拖期惩罚成本和最大化产品合格率为优化目标的设备维护与柔性作业车间调度问题集成优化模型。提出了一种新的层次生物地理算法进行求解,...针对MTO(Make To Order)生产模式下,设备故障带来的拖期交货和产品质量下降等问题,建立了以最小化拖期惩罚成本和最大化产品合格率为优化目标的设备维护与柔性作业车间调度问题集成优化模型。提出了一种新的层次生物地理算法进行求解,算法将解空间分为上、下两层,设计了分层迭代寻优机制,融入多种局域搜索策略,提高了收敛速度和寻优精度。与遗传算法和生物地理学优化算法对比,计算结果表明层次生物地理算法优于其他两种算法。展开更多
It is well known that the kit completeness of parts processed in the previous stage is crucial for the subsequent manufacturing stage.This paper studies the flexible job shop scheduling problem(FJSP)with the objective...It is well known that the kit completeness of parts processed in the previous stage is crucial for the subsequent manufacturing stage.This paper studies the flexible job shop scheduling problem(FJSP)with the objective of material kitting,where a material kit is a collection of components that ensures that a batch of components can be ready at the same time during the product assembly process.In this study,we consider completion time variance and maximumcompletion time as scheduling objectives,continue the weighted summation process formultiple objectives,and design adaptive weighted summation parameters to optimize productivity and reduce the difference in completion time between components in the same kit.The Soft Actor Critic(SAC)algorithm is designed to be combined with the Adaptive Multi-Buffer Experience Replay(AMBER)mechanism to propose the SAC-AMBER algorithm.The AMBER mechanism optimizes the experience sampling and policy updating process and enhances learning efficiency by categorically storing the experience into the standard buffer,the high equipment utilization buffer,and the high productivity buffer.Experimental results show that the SAC-AMBER algorithm can effectively reduce the maximum completion time on multiple datasets,reduce the difference in component completion time in the same kit,and thus optimize the readiness of the part kits,demonstrating relatively good stability and convergence.Compared with traditional heuristics,meta-heuristics,and other deep reinforcement learning methods,the SAC-AMBER algorithm performs better in terms of solution quality and computational efficiency,and through extensive testing on multiple datasets,the algorithm has been confirmed to have good generalization ability,providing an effective solution to the FJSP problem.展开更多
单目标柔性作业车间调度问题是经典作业车间调度问题的重要扩展,对其的研究有着重要的理论意义和工程实践意义。首先对单目标柔性作业车间调度问题进行了描述;然后结合FISP问题,改进设计了遗传算法;接着应用Visual Studio 2008开发工具...单目标柔性作业车间调度问题是经典作业车间调度问题的重要扩展,对其的研究有着重要的理论意义和工程实践意义。首先对单目标柔性作业车间调度问题进行了描述;然后结合FISP问题,改进设计了遗传算法;接着应用Visual Studio 2008开发工具设计开发了原型系统,并对系统进行了测试,得到了运行结果;最后对全文工作进行了总结。展开更多
文摘针对MTO(Make To Order)生产模式下,设备故障带来的拖期交货和产品质量下降等问题,建立了以最小化拖期惩罚成本和最大化产品合格率为优化目标的设备维护与柔性作业车间调度问题集成优化模型。提出了一种新的层次生物地理算法进行求解,算法将解空间分为上、下两层,设计了分层迭代寻优机制,融入多种局域搜索策略,提高了收敛速度和寻优精度。与遗传算法和生物地理学优化算法对比,计算结果表明层次生物地理算法优于其他两种算法。
文摘It is well known that the kit completeness of parts processed in the previous stage is crucial for the subsequent manufacturing stage.This paper studies the flexible job shop scheduling problem(FJSP)with the objective of material kitting,where a material kit is a collection of components that ensures that a batch of components can be ready at the same time during the product assembly process.In this study,we consider completion time variance and maximumcompletion time as scheduling objectives,continue the weighted summation process formultiple objectives,and design adaptive weighted summation parameters to optimize productivity and reduce the difference in completion time between components in the same kit.The Soft Actor Critic(SAC)algorithm is designed to be combined with the Adaptive Multi-Buffer Experience Replay(AMBER)mechanism to propose the SAC-AMBER algorithm.The AMBER mechanism optimizes the experience sampling and policy updating process and enhances learning efficiency by categorically storing the experience into the standard buffer,the high equipment utilization buffer,and the high productivity buffer.Experimental results show that the SAC-AMBER algorithm can effectively reduce the maximum completion time on multiple datasets,reduce the difference in component completion time in the same kit,and thus optimize the readiness of the part kits,demonstrating relatively good stability and convergence.Compared with traditional heuristics,meta-heuristics,and other deep reinforcement learning methods,the SAC-AMBER algorithm performs better in terms of solution quality and computational efficiency,and through extensive testing on multiple datasets,the algorithm has been confirmed to have good generalization ability,providing an effective solution to the FJSP problem.
文摘单目标柔性作业车间调度问题是经典作业车间调度问题的重要扩展,对其的研究有着重要的理论意义和工程实践意义。首先对单目标柔性作业车间调度问题进行了描述;然后结合FISP问题,改进设计了遗传算法;接着应用Visual Studio 2008开发工具设计开发了原型系统,并对系统进行了测试,得到了运行结果;最后对全文工作进行了总结。
