Manufacturing systems are increasingly required to operate in high-mix, low-volume production environments, where process flexibility is crucial. One effective way to achieve this flexibility is through the use of multiple processing alternatives (MPA), allowing a product to be produced using different process plans or component structures. In MPA environments, scheduling decisions must address both the selection of processing alternatives for each product and the execution order of the resulting production tasks. Additionally, processing times often vary due to machine conditions and process variability, further complicating scheduling. This study introduces a dual-network-based deep reinforcement learning method for scheduling in manufacturing systems with multiple processing alternatives. The framework utilizes two Q-networks to learn both the selection of processing alternatives and the dispatching rules. Computational experiments demonstrate that the proposed method effectively reduces both the average makespan and its variability compared to a genetic algorithm-based approach, particularly as the problem size increases, showcasing its effectiveness in the face of processing time uncertainty.

The purpose of the study was to evaluate the lumbar mobility and flexibility by the vertical vibration stimulation. The subjects were 21 young adults were divided into vibration group (n = 7) that applied 30 Hz vibration stimulation to the lumbar, foam roller group (n = 7) that relaxes the lumbar muscles with a foam roller, and good morning exercise group (n = 7) that stimulates the lumbar spine with the good morning exercise. The muscle strength, EMG and the sit & reach test were measured, to evaluate the lumbar mobility and flexibility before and after exercise intervention in each group. Results showed increasing in the vibrating group in muscle strength and EMG, and the good morning group and the vibrating group in the Sit & Reach test. This can be developed as a new alternative to exercise therapy for spine rehabilitation.