Research On Integrated Scheduling Methods Of AGV And Processing Machine

The State Council proposed "Made in China 2025",marking the beginning of the transformation of our country’s manufacturing industry to intelligent manufacturing.The development of science and technology as well as the intensification of market competition have brought tremendous changes to industrial production and consumer markets.In the past,large-scale production centered on manufacturers gradually transform into a model of multi-variety,small-batch production based on consumer orders.In this flexible and complex production mode,scheduling as an important means of optimizing resource allocation in the workshop plays a vital role in improving production efficiency and reducing production costs,and has become a key part of intelligent production management.The data shows that 95% of the time during the operation of the workpiece is in the state of transportation,waiting for transportation and waiting for processing.Automatic Guided Vehicle(AGV)which integrates a variety of advanced technologies has the characteristics of high flexibility and high automation is the core equipment in the intelligent workshop logistics system.Considering the collaborative work of AGV and processing machines can optimize the production process from a global perspective and coordinate the allocation of workshop resources.This problem is a typical NP-hard problem with huge and complex solution space.In order to reduce the difficulty of the problem,some scholars do not consider the path conflicts encountered during the operation of the AGV.However,the path conflicts in the multi-AGV system are unavoidable.If the path conflict problem is not resolved,it will lead to path deadlock even paralysis of the production system.The AGV and processing integration scheduling that considers path conflicts is closer to the actual production,and has stronger guidance for the digital workshop.Therefore,the research on this subject has important theoretical significance and engineering value.The thesis focuses on the integrated scheduling of AGV and processing machines considering path conflicts.The main contents of the research are as follows:Firstly,this thesis sorts out and summarizes the research status and development trend of the integrated scheduling of AGVs and processing machines,and points out the deficiencies in the current research and the research purpose of this thesis.Secondly,the basic theory of the algorithm studied in this thesis is introduced.In order to solve the path conflict problem,the Dijkstra algorithm based on time window is introduced.At the same time,the traditional workshop scheduling problem is described,and the characteristics of the problem studied in this thesis are emphasized,meanwhile a mathematical model that meets the constraints is established according to its characteristics.Thirdly,the single-objective scheduling problem of AGVs and processing machine integration is studied.A discrete whale optimization algorithm is proposed,with the goal of minimizing the maximum completion time,a mathematical model is established,in the meantime a three-stage coding is used to realize the integrated coding of AGV and machine,and the relationship between continuous space and discrete space is established.Secondly,in order to ensure the quality and diversity of the initial population,an extended GLR population initialization method combining chaotic mapping and opposition learning is designed.Then,the Levy flight operator and threshold restart operation are used to further improve the overall performance of the algorithm search ability.Finally,in order to improve the local search ability of the algorithm,a variable neighborhood search algorithm combining the characteristics of the problem is introduced.Fourthly,a multi-objective scheduling optimization model,which addresses three optimization goals,such as the maximum completion time,AGV running time and total machine load,is established based on the AGV conflict-free path.According to the search requirements of the algorithm in different periods,a cross-recombination strategy through the adaptive clustering algorithm based on hamming distance and the nonlinear convergence factor including adaptive individual cross-probability is proposed.In order to avoid the algorithm falling into the local optimum in the later iterations,the adaptive population mutation probability is designed,and a disturbance factor is added.In addition,the environment selection strategy based on the number of grid divisions and the expansion of the grid distance combined with the non-dominated level is introduced to enhance the distribution of the solution set.Finally,the algorithm proposed in this thesis is verified by standard calculation examples and calculation examples in existing research results.The comparison of the results shows that the proposed algorithm has certain feasibility and superiority.