Research On Intelligent Warp Scheduling Strategy For Weaving Enterprises

With the change of market demand and the increase of personalized choices by consumers,the production mode of weaving enterprises has gradually shifted from mass production to multi variety and small batch production.In multi variety and small batch production,each product has its unique process flow,processing time,delivery time,and other factors to consider,so there will be more requirements for production scheduling.Currently,most weaving enterprises still use traditional manual production scheduling methods in their warping preparation workshops,which is inefficient.In this case,this paper uses intelligent algorithms to optimize the warping production scheduling plan of weaving enterprises,in order to solve the problem of solving the optimal solution in the multi variety and small batch production mode.The main research contents of this paper are as follows:(1)A genetic algorithm based optimization method for order scheduling and grouping is proposed to address the characteristics of multiple orders and yarn counts in weaving enterprises.Build a set of order scheduling groups through genetic algorithm crossover and mutation rules,and then calculate the scheduling axis based on the grouping results to establish a joint solution set of scheduling axis and scheduling group to guide genetic algorithm optimization iteration to the optimal group.(2)This paper analyzes the warping production process of weaving enterprises,and establishes a master-slave correlation optimization model of relations of production in warping preparation workshop that meets the conditions of cylinder assembly scheduling,taking the number of open shafts and processing time as the objective function,considering the constraints such as the total number of warps,the order meters,the winding length of warping shafts,and the number of warping shafts.(3)Propose an improved simulated annealing algorithm for scheduling and axis calculation,which improves the annealing plan and sampling process of the standard simulated annealing algorithm.The improved simulated annealing algorithm,simulated annealing algorithm,and genetic algorithm were tested using standard test functions,and the results showed that the convergence speed and search accuracy of the improved simulated annealing algorithm were improved.(4)Validate the improved simulated annealing algorithm through production scheduling simulation.Apply the improved simulated annealing algorithm to the production scheduling problem in the warping workshop.In order to verify its excellence,compare and analyze its calculation results with simulated annealing algorithm and genetic algorithm.The experimental results show that the improved simulated annealing algorithm has faster convergence speed and better optimization effect in solving the scheduling problem of warping production.(5)According to the production scheduling needs of enterprises,an intelligent production scheduling system has been designed and developed to achieve user-friendly interface functions and effective backend logic.Apply improved simulated annealing algorithm in the system to enable users to quickly generate suitable scheduling plans to meet their production needs.