A Shared Multi-buffer Banyan Network is presented in this letter. Its control algorithm and switching fabric are simple, and it fits the high speed ATM network well. The simulation results show that the throughput of ...A Shared Multi-buffer Banyan Network is presented in this letter. Its control algorithm and switching fabric are simple, and it fits the high speed ATM network well. The simulation results show that the throughput of the proposed model is high.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
文摘A Shared Multi-buffer Banyan Network is presented in this letter. Its control algorithm and switching fabric are simple, and it fits the high speed ATM network well. The simulation results show that the throughput of the proposed model is high.
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.
