Research On Multi-plant Production Scheduling In Distributed Manufacturing Environment

With the rapid development of manufacturing globalization,the production model of distributed manufacturing has become more and more common.The complexity of the distributed manufacturing environment further increases the difficulty of scheduling solutions.This thesis studies the problem of multi-factory production scheduling in a distributed manufacturing environment,with a view to achieving energy conservation and consumption reduction,cost reduction,and improving the overall efficiency and benefits of distributed systems through coordination and job optimization among multiple factories.In this thesis,summarizes the domestic and foreign research status of distributed multifactory production scheduling problems,analyzes the research focus and difficulties of distributed multi-factory scheduling problems,and analyzes the problems in the current research;research on production scheduling genetic algorithm A more in-depth study on the status quo and development trends.On this basis,taking into account factors such as multi-factory,interfactory workpiece transfer time processing workpiece transfer time and other factors,a mathematical model of distributed multi-factory scheduling problem is established.In the distributed multi-factory environment,the production scheduling system involves many physical objects and complex information relationships.This thesis combs the information characteristics of the main modules in the actual physical system of the distributed multi-factory and the information relationship between them.Based on the information physics system,a distributed scheduling system physics-information architecture is established.All factories are defined as parallel information agents.The information exchange between factories has equal status and data sharing.Through the interaction and collaboration of information,each factory and each other are managed and scheduled.Between homework.On this basis,the edge-based computing model is further established.The information physics system establishes the network foundation for the edge computing model.The edge computing model further encapsulates the multi-factory physical components,and defines the organizational model of the workshop layer,the factory layer,and the workshop layer and The relationship model at the factory level establishes a problem-solving model for a multi-factory collaborative scheduling system in a distributed manufacturing environment.In a distributed multi-factory environment,because many jobs flow in different factories,there are more constraints and resource restrictions than the single-factory environment,which is prone to a large number of machine idle problems.At the same time,due to the increased complexity of the problem,various factors interfere,Increasing the difficulty of solving problems,it is crucial to solve problems quickly and accurately.To this end,this thesis proposes an improved adaptive genetic algorithm for batch partition fusion.Batch division resolves large batches into small batches,making it easier to schedule their production;adaptive genetic algorithms adapt the genetic and cross-probability adaptive changes to make the algorithm converge faster.In order to verify the effectiveness of the improved adaptive genetic algorithm for merging batches proposed in this thesis,on the basis of simulation experiments on a large number of data,the initial values of the crossover and mutation probability of the improved adaptive genetic algorithm are obtained,and the data in the literature are further used.A simulation study of the proposed batch partition fusion improved adaptive genetic algorithm was conducted and compared with the results in references.The results show that the large amount of idle waiting time in the production machine during the scheduling process can be reduced by the method proposed in this thesis,and the maximum completion time is shortened indirectly by the indirect impact.In this thesis,the batch division fusion improved adaptive genetic algorithm has the advantages of adaptive adjustment of crossover and mutation probability,and rapid jump out of local convergence.