Research On Reconstruction Method Of Multi-customer Variable Beat Mixed Flow Production Line In Shoe Workshop

At present,in order to reduce the unit price and logistics cost of order purchase in order to achieve the batch scale effect of each order,shoe companies mostly use batch purchase materials,and at the same time,in order to reduce production costs,they do not combine market forecasts,and directly use the minimized beat for mass production,so as to prepare goods in advance.This model is indeed feasible when market orders are stable or when the company’s sales volume is growing;However,as the coronavirus pandemic fluctuates with each other,the market is unpredictable,demand uncertainty is increasing,and once the market is volatile,the problem of high inventory occurs.Therefore,how to respond quickly to the market while improving capacity utilization and reducing inventory in a changing market is becoming more and more important.This paper adopts mathematical analysis method,and establishes the relationship parameters of the relevant variables of each problem through the method of mathematical model to find the optimal solution of the problem.In addition,a heuristic method is adopted,the advantages of genetic algorithm and annealing algorithm are synthesized,an adaptive algorithm is designed,and a hybrid genetic algorithm is improved.The main research work of this paper is as follows:(1)Aiming at the production line balance problem under uncertain conditions,a dynamic optimization model is established through robust optimization;Then,according to the product life cycle,the product demand analysis is carried out to achieve the initial balance model of minimizing the cycle time and minimizing the cost;Finally,through conditional assumptions,the model is simplified to construct the secondary balance of the production line.(2)The process of improving the algorithm is emphasized,mainly including: using decimal coding;The initial population is generated by topological ranking theory and random search method.Build fitness functions using multiple objective functions;Add "simulated annealing algorithm" to avoid local optimization;Two-point crossover and elementary bit variation are used;An adaptive algorithm is designed to improve.Finally,two different literatures are selected for example testing,which verifies the effectiveness of the algorithm.(3)Using the actual shoe making case for algorithm testing,and then through Flexsim parameter setting,establish a simulation model before and after optimization,output a report,and compare the results;The results show that the capacity utilization rate can be increased from 80.43% to 90.47%,an increase of 10.04%.The innovations in this article are as follows:1)Propose an initial balance model based on product life cycle to analyze product demand to minimize the cycle time and minimize the cost;2)A hybrid genetic algorithm with improved adaptive operator and "simulated annealing" is proposed,so that the parent of the "stable range" will not be missed,and the algorithm iteration result is better.