Aircraft assembly is characterized by stringent precedence constraints,limited resource availability,spatial restrictions,and a high degree of manual intervention.These factors lead to considerable variability in oper...Aircraft assembly is characterized by stringent precedence constraints,limited resource availability,spatial restrictions,and a high degree of manual intervention.These factors lead to considerable variability in operator workloads and significantly increase the complexity of scheduling.To address this challenge,this study investigates the Aircraft Pulsating Assembly Line Scheduling Problem(APALSP)under skilled operator allocation,with the objective of minimizing assembly completion time.A mathematical model considering skilled operator allocation is developed,and a Q-Learning improved Particle Swarm Optimization algorithm(QLPSO)is proposed.In the algorithm design,a reverse scheduling strategy is adopted to effectively manage large-scale precedence constraints.Moreover,a reverse sequence encoding method is introduced to generate operation sequences,while a time decoding mechanism is employed to determine completion times.The problem is further reformulated as a Markov Decision Process(MDP)with explicitly defined state and action spaces.Within QLPSO,the Q-learning mechanism adaptively adjusts inertia weights and learning factors,thereby achieving a balance between exploration capability and convergence performance.To validate the effectiveness of the proposed approach,extensive computational experiments are conducted on benchmark instances of different scales,including small,medium,large,and ultra-large cases.The results demonstrate that QLPSO consistently delivers stable and high-quality solutions across all scenarios.In ultra-large-scale instances,it improves the best solution by 25.2%compared with the Genetic Algorithm(GA)and enhances the average solution by 16.9%over the Q-learning algorithm,showing clear advantages over the comparative methods.These findings not only confirm the effectiveness of the proposed algorithm but also provide valuable theoretical references and practical guidance for the intelligent scheduling optimization of aircraft pulsating assembly lines.展开更多
Objective To study the schedule dependent reversion of cis diamminedichloroplatinum (CDDP) resistance by 5 fluorouracil (5 Fu) in a CDDP resistant human lung adenocarcinoma cell line A 549 DDP . Metho...Objective To study the schedule dependent reversion of cis diamminedichloroplatinum (CDDP) resistance by 5 fluorouracil (5 Fu) in a CDDP resistant human lung adenocarcinoma cell line A 549 DDP . Methods Dimethylthiazol dipheryltetrazolium bromide (MTT) assay and immunocytochemistry were used. Results After the A 549 DDP was treated with CDDP, followed immediately by exposure to 5 Fu, cytotoxicity of CDDP increased 1.8 fold. After pretreatment of A 549 DDP with 5 Fu, followed immediately by exposure to CDDP, the cytotoxicity of CDDP increased 3.9 fold. After pretreatment of A 549 DDP with 5 Fu, after a 24 or 48 hour drug free interval, followed by exposure to CDDP, the cytotoxicity of CDDP increased 20 and 250 fold, respectively, and the A 549 DDP was rendered more sensitive than its parental cell line A 549 . In parallel with the increased cytotoxicity, the cellular GSH content was significantly reduced at 24 or 48 hour after 5 Fu pretreatment. However, depletion of GSH by buthionine sulfoximine (BSO) only resulted in partial reversion of CDDP resistance. 5 Fu could also inhibit the expression of MRP, but had no effect on the expression of GSTπ. The effect of 5 Fu on the parental cell line A 549 was much smaller than that in A 549 DDP . Conclusion Scheduled administration of 5 Fu can reverse CDDP resistance completely through reduction of GSH and inhibition of MRP expression.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52475543)Natural Science Foundation of Henan(Grant No.252300421101)+1 种基金Henan Province University Science and Technology Innovation Talent Support Plan(Grant No.24HASTIT048)Science and Technology Innovation Team Project of Zhengzhou University of Light Industry(Grant No.23XNKJTD0101).
文摘Aircraft assembly is characterized by stringent precedence constraints,limited resource availability,spatial restrictions,and a high degree of manual intervention.These factors lead to considerable variability in operator workloads and significantly increase the complexity of scheduling.To address this challenge,this study investigates the Aircraft Pulsating Assembly Line Scheduling Problem(APALSP)under skilled operator allocation,with the objective of minimizing assembly completion time.A mathematical model considering skilled operator allocation is developed,and a Q-Learning improved Particle Swarm Optimization algorithm(QLPSO)is proposed.In the algorithm design,a reverse scheduling strategy is adopted to effectively manage large-scale precedence constraints.Moreover,a reverse sequence encoding method is introduced to generate operation sequences,while a time decoding mechanism is employed to determine completion times.The problem is further reformulated as a Markov Decision Process(MDP)with explicitly defined state and action spaces.Within QLPSO,the Q-learning mechanism adaptively adjusts inertia weights and learning factors,thereby achieving a balance between exploration capability and convergence performance.To validate the effectiveness of the proposed approach,extensive computational experiments are conducted on benchmark instances of different scales,including small,medium,large,and ultra-large cases.The results demonstrate that QLPSO consistently delivers stable and high-quality solutions across all scenarios.In ultra-large-scale instances,it improves the best solution by 25.2%compared with the Genetic Algorithm(GA)and enhances the average solution by 16.9%over the Q-learning algorithm,showing clear advantages over the comparative methods.These findings not only confirm the effectiveness of the proposed algorithm but also provide valuable theoretical references and practical guidance for the intelligent scheduling optimization of aircraft pulsating assembly lines.
文摘Objective To study the schedule dependent reversion of cis diamminedichloroplatinum (CDDP) resistance by 5 fluorouracil (5 Fu) in a CDDP resistant human lung adenocarcinoma cell line A 549 DDP . Methods Dimethylthiazol dipheryltetrazolium bromide (MTT) assay and immunocytochemistry were used. Results After the A 549 DDP was treated with CDDP, followed immediately by exposure to 5 Fu, cytotoxicity of CDDP increased 1.8 fold. After pretreatment of A 549 DDP with 5 Fu, followed immediately by exposure to CDDP, the cytotoxicity of CDDP increased 3.9 fold. After pretreatment of A 549 DDP with 5 Fu, after a 24 or 48 hour drug free interval, followed by exposure to CDDP, the cytotoxicity of CDDP increased 20 and 250 fold, respectively, and the A 549 DDP was rendered more sensitive than its parental cell line A 549 . In parallel with the increased cytotoxicity, the cellular GSH content was significantly reduced at 24 or 48 hour after 5 Fu pretreatment. However, depletion of GSH by buthionine sulfoximine (BSO) only resulted in partial reversion of CDDP resistance. 5 Fu could also inhibit the expression of MRP, but had no effect on the expression of GSTπ. The effect of 5 Fu on the parental cell line A 549 was much smaller than that in A 549 DDP . Conclusion Scheduled administration of 5 Fu can reverse CDDP resistance completely through reduction of GSH and inhibition of MRP expression.
