Steel Plant Crown Modeling And Scheduling Optimization Based On Genetic Algorithm

With the introduction of the 14th Five-Year Plan and the Long-Range Objectives Through the Year 2035,“new infrastructure” industry closely related to steel will usher in explosive growth.In this context,the increase in steel output may put higher requirements on the efficiency of factory production scheduling.In the production blocks of steelmakingcontinuous casting,overhead crane has served as the main production workshop transportation means due to little influence from the ground environment,high operating load,and stable lifting.However,the overhead crane requires to consider the situation that several cranes may perform tasks at the same time when scheduling,which leads to low efficiency of traditional manual scheduling.To optimize enterprise resource allocation,improve production efficiency,and reduce production costs,this thesis focuses on limitations of the continuous casting production workshop,including multiple machines,multiple tasks,space,and time.On this basis,it tries to establish a model of a steel mill workshop,and explore an effective method for optimizing production scheduling.First,It attempts to analyze the task characteristics of overhead crane in the steel plant,model the abstraction of elements in the workshop,and set rules for the overhead crane model,such as attribute rules,crane selection rules,avoidance rules and so on.In terms of the attribute rule,it is an important part of abstract description of overhead crane and workshop elements.In the simulation process,recording attribute changes play a vital role in displaying optimization sequences and depicting optimization results.As for the selection rule,it means to assign tasks for overhead crane with a higher matching degree.During the operation of the overhead crane,avoidance is required due to space limitations.Therefore,the avoidance rule should be set to make reasonable movements in need of avoidance.Secondly,An improved genetic algorithm is designed for optimizing the overhead crane scheduling model.In this context,the bat algorithm is introduced to solve the problem that the improved genetic algorithm is highly dependent on the initial population.Meanwhile,the adaptive genetic algorithm with catastrophic links is designed to enable the algorithm to jump out of the local optimum.With the overhead crane sequence as the chromosome code and with the continuous casting workshop of a new steel workshop as the object,a feasible and optimized scheduling plan is generated after multiple iterations of calculation.Currently,few studies focus on the communication between simulation results and actual PLC to transfer calculation results.To this end,the method of communication based on Siemens virtual PLC relying on TCP/IP protocol is studied.On this basis,the optimization results are converted into overhead crane control commands and transmitted to the PLC.Meanwhile,the optimization results can be checked through the simulation software.According to the research results,the optimal scheduling method proposed in this thesis is reasonable and effective.The adaptive genetic algorithm with catastrophic links perform excellently in the face of multi-machine and multi-task scheduling.Such a method can be applied to assist in other workshop scheduling of the plant,and it provides reference value for overhead crane scheduling of manufacturing enterprises.Finally the communication method of the upper computer is of great significance for improving the implementation of optimization results and improving the level of enterprise digitization.