Multi-step Multi-unit Cold Rolling Scheduling Optimization Method And Optimization Scheduling System Development

As one of the key processes in the production of steel products,cold rolling is the most downstream in the whole steel product production process,which plays a vital role during the delivery period of steel.There is a huge space to optimize in its large-scale,multi-process,multi-unit continuous production process.The scheduling results for each process equipment will play an important role in optimizing the overall contract planning schedule.The multi-process and multi-unit cold rolling scheduling problem is similar to the Travelling Salesman Problem(TSP)of time window in the field of combinatorial optimization research,whose essence is the decision on selecting equipment for the decision material,the production time.While to some extent,the time window limits order and location of materials on the equipment.According to the production characteristics of cold-rolled steel coils,this thesis studies the optimization of multi-process and multi-unit cold rolling schedules.Through decision-making,the equipment for coil production,the batch and the production sequence on the equipment are optimized,so as to improve the utilization rate of equipment,scheduling quality contract fulfillment rate,equipment preference and reduce usage of transition volumes.In this thesis,the research on the optimization of multi-process multi-unit cold rolling schedule is carried out.The main work is as follows:(1)A mixed integer programming model is built for the multi-process and multi-unit cold rolling scheduling problem.A heuristic algorithm is designed to generate the initial solution by learning from the experience of the planner.Experimental tests are carried out through actual data from steel companies.The results show that the initial solution KPI is superior to the schedule KPI given by the scheduler in most cases.(2)In order to further optimize the model target KPI,the tabu simulation annealing algorithm is designed to optimize the initial solution,and many experiments are designed.The improvement effect on the initial solution is obvious.The traditional optimization algorithm is used to solve the multi-process multi-unit cold.Rolling schedule problem.(3)The iterative greedy algorithm is designed to improve the initial solution.The comparison experiment with the tabu simulation annealing algorithm is added to test the degree of improvement of the initial solution by two different algorithms under different sets of the same entry data.(4)Based on the above optimization algorithm,a multi-process multi-unit cold rolling scheduling optimization system is developed.The initial solution and optimization algorithm are packaged into the system,and the Gantt chart is designed to visualize the scheduling results.In this thesis,the mathematical model is established for the multi-process multi-unit cold rolling scheduling problem,and the heuristic algorithm is designed to generate the initial solution.At the same time,the tabu simulation annealing algorithm and the iterative greedy algorithm are used to optimize.Under the premise of satisfying all time windows and other hard constraints,soft constraints such as equipment preference between units are also dealt with accordingly,which solves the problem of multi-process and multi-unit cold rolling scheduling,greatly enhances the KPI of the results,and tests the advantages and disadvantages of the two optimization algorithms.