Research on Flexible Job Shop Scheduling Based on Improved Two-Layer Optimization Algorithm

To improve the productivity,the resource utilization and reduce the production cost of flexible job shops,this paper designs an improved two-layer optimization algorithm for the dual-resource scheduling optimization problem of flexible job shop considering workpiece batching.Firstly,a mathematical model is established to minimize the maximum completion time.Secondly,an improved two-layer optimization algorithm is designed:the outer layer algorithm uses an improved PSO(Particle Swarm Optimization)to solve the workpiece batching problem,and the inner layer algorithm uses an improved GA(Genetic Algorithm)to solve the dual-resource scheduling problem.Then,a rescheduling method is designed to solve the task disturbance problem,represented by machine failures,occurring in the workshop production process.Finally,the superiority and effectiveness of the improved two-layer optimization algorithm are verified by two typical cases.The case results show that the improved two-layer optimization algorithm increases the average productivity by 7.44% compared to the ordinary two-layer optimization algorithm.By setting the different numbers of AGVs(Automated Guided Vehicles)and analyzing the impact on the production cycle of the whole order,this paper uses two indicators,the maximum completion time decreasing rate and the average AGV load time,to obtain the optimal number of AGVs,which saves the cost of production while ensuring the production efficiency.This research combines the solved problem with the real production process,which improves the productivity and reduces the production cost of the flexible job shop,and provides new ideas for the subsequent research.

Research on Flexible Job Shop Scheduling Optimization Based on Segmented AGV

As a typical transportation tool in the intelligent manufacturing system,Automatic Guided Vehicle(AGV)plays an indispensable role in the automatic production process of the workshop.Therefore,integrating AGV resources into production scheduling has become a research hotspot.For the scheduling problem of the flexible job shop adopting segmented AGV,a dual-resource scheduling optimization mathematical model of machine tools and AGVs is established by minimizing the maximum completion time as the objective function,and an improved genetic algorithmis designed to solve the problem in this study.The algorithmdesigns a two-layer codingmethod based on process coding and machine tool coding and embeds the task allocation of AGV into the decoding process to realize the real dual resource integrated scheduling.When initializing the population,three strategies are designed to ensure the diversity of the population.In order to improve the local search ability and the quality of the solution of the genetic algorithm,three neighborhood structures are designed for variable neighborhood search.The superiority of the improved genetic algorithmand the influence of the location and number of transfer stations on scheduling results are verified in two cases.

Glutaredoxin-1 alleviates acetaminophen-induced liver injury by decreasing its toxic metabolites

Excessive N-acetyl-p-benzoquinone imine(NAPQI)formation is a starting event that triggers oxidative stress and subsequent hepatocyte necrosis in acetaminophen(APAP)overdose caused acute liver failure(ALF).S-glutathionylation is a reversible redox post-translational modification and a prospective mechanism of APAP hepatotoxicity.Glutaredoxin-1(Glrx1),a glutathione-specific thioltransferase,is a primary enzyme to catalyze deglutathionylation.The objective of this study was to explored whether and how Glrx1 is associated with the development of ALF induced by APAP.The Glrx1 knockout mice(Glrx1^(-/-))and liver-specific overexpression of Glrx1(AAV8-Glrx1)mice were produced and underwent APAPinduced ALF.Pirfenidone(PFD),a potential inducer of Glrx1,was administrated preceding APAP to assess its protective effects.Our results revealed that the hepatic total protein S-glutathionylation(PSSG)increased and the Glrx1 level reduced in mice after APAP toxicity.Glrx1^(-/-)mice were more sensitive to APAP overdose,with higher oxidative stress and more toxic metabolites of APAP.This was attributed to Glrx1 deficiency increasing the total hepatic PSSG and the S-glutathionylation of cytochrome p4503a11(Cyp3a11),which likely increased the activity of Cyp3a11.Conversely,AAV8-Glrx1 mice were defended against liver damage caused by APAP overdose by inhibiting the S-glutathionylation and activity of Cyp3a11,which reduced the toxic metabolites of APAP and oxidative stress.PFD precede administration upregulated Glrx1 expression and alleviated APAP-induced ALF by decreasing oxidative stress.We have identified the function of Glrx1 mediated PSSG in liver injury caused by APAP overdose.Increasing Glrx1 expression may be investigated for the medical treatment of APAP-caused hepatic injury.

Research on Optimization of Dual-Resource Batch Scheduling in Flexible Job Shop

With the rapid development of intelligent manufacturing and the changes in market demand,the current manufacturing industry presents the characteristics of multi-varieties,small batches,customization,and a short production cycle,with the whole production process having certain flexibility.In this paper,a mathematical model is established with the minimum production cycle as the optimization objective for the dual-resource batch scheduling of the flexible job shop,and an improved nested optimization algorithm is designed to solve the problem.The outer layer batch optimization problem is solved by the improved simulated annealing algorithm.The inner double resource scheduling problem is solved by the improved adaptive genetic algorithm,the double coding scheme,and the decoding scheme of Automated Guided Vehicle(AGV)scheduling based on the scheduling rules.The time consumption of collision-free paths is solved with the path planning algorithm which uses the Dijkstra algorithm based on a time window.Finally,the effectiveness of the algorithm is verified by actual cases,and the influence of AGV with different configurations on workshop production efficiency is analyzed.

Optimization of CNC Turning Machining Parameters Based on Bp-DWMOPSO Algorithm

Cutting parameters have a significant impact on the machining effect.In order to reduce the machining time and improve the machining quality,this paper proposes an optimization algorithm based on Bp neural networkImproved Multi-Objective Particle Swarm(Bp-DWMOPSO).Firstly,this paper analyzes the existing problems in the traditional multi-objective particle swarm algorithm.Secondly,the Bp neural network model and the dynamic weight multi-objective particle swarm algorithm model are established.Finally,the Bp-DWMOPSO algorithm is designed based on the established models.In order to verify the effectiveness of the algorithm,this paper obtains the required data through equal probability orthogonal experiments on a typical Computer Numerical Control(CNC)turning machining case and uses the Bp-DWMOPSO algorithm for optimization.The experimental results show that the Cutting speed is 69.4 mm/min,the Feed speed is 0.05 mm/r,and the Depth of cut is 0.5 mm.The results show that the Bp-DWMOPSO algorithm can find the cutting parameters with a higher material removal rate and lower spindle load while ensuring the machining quality.This method provides a new idea for the optimization of turning machining parameters.