摘要
为解决织造企业整经准备车间在多约束条件下的生产效率低下及经轴利用率低的问题,在考虑经纱总根数、订单米数、经轴绕卷纱线长度、经轴绕卷纱线根数等约束条件的情况下,以开轴数和加工时间为目标函数,建立满足拼缸条件的整经准备车间生产关系主从优化关联模型。提出了基于自适应模拟退火算法的整经准备车间生产调度方案,通过引入自适应退火因子有效解决传统模拟退火算法计算效率低及容易陷入局部最优解的缺点,并根据企业实际生产案例进行仿真实验。结果表明:该自适应模拟退火算法能够有效地提升纺织企业整经生产调度效率,提升经轴利用率;且该方法提升整经准备车间运行效率和经轴利用率的能力要优于其它算法。
Objective Taking the warping scheduling of weaving enterprises as the research object,the yarn types and order quantities are reasonably configured through intelligent algorithms based on the actual scheduling needs,aiming to achieve the optimization of production scheduling,improve production efficiency,reduce waste of raw materials,and increase production capacity.In order to solve the problems of low production efficiency and low utilization of warping shafts in warping department of weaving enterprises under multiple constraint conditions,the optimization achieved through intelligent algorithms in configuring yarn types and order quantities can significantly enhance the efficiency of the entire process flow,thereby realizing an increase in production capacity.Method Taking the total number of warp ends,order meters,winding length of warp beam,warp ends per warp beam and others as the constraints,and the number of warp beams and processing time as the objective functions,the master-slave optimization correlation model of production relations in warping department meeting the cylinder assembly conditions was established.A production scheduling scheme of the warping department based on adaptive simulated annealing algorithm was proposed.By introducing an adaptive annealing factor,the shortcomings of conventional simulated annealing algorithms such as low computational efficiency and susceptibility to local optima were effectively overcome.Results In order to verify that the master-slave model and its solution algorithm of multi-constraints production scheduling are suitable for warp beam preparation,the production order of a certain production cycle in the warping department of a textile enterprise was taked as an example.the adaptive simulated annealing algorithm(ASAA),simulate anneal algorithm(SAA)and genetic algorithm(GA)were adopted to solve the orders.The smaller the number of warp beams,the shorter the processing time and algorithm running time,the better the algorithm optimization effect.According to the data of an individual order,the experimental simulation was carried out with the number of warp beams,processing time and algorithm running time as the objectives.The utilization rate of the warp beam obtained by using the ASAA proposed in this paper was in general better than the other two algorithms,and the solution obtained in 30 experiments was less volatile and more stable.The solution sets of the three algorithms for the 30 experiments with two objective functions as the coordinate axis.Smaller values of the two objective functions would indicate better solution.The solution set of ASAA was more concentrated on the lower left part,showing that the solution set of ASAA yielded smaller objective functions with better performance.It was evident that ASAA outperformed the other two algorithms in terms of computational efficiency when solving the warping scheduling problem.For the same order,in 30 experiments,the ASAA could save an average of 20%of the operation time compared with the other two algorithms.Conclusion Based on the research on the production scheduling problem in warping department of weaving enterprises,an optimization model of warping production scheduling for multi-constraint conditions and warp beam production is constructed,and simulation experiments are carried out according to the actual production cases of enterprises.The results show that the adaptive simulated annealing algorithm proposed in this paper can effectively improve the efficiency of warping production scheduling in textile enterprises,and improve the utilization of warp beams.However,considering that the actual production process would be much more complex than the experimental environment,and unstable factors such as order insertion,equipment failure,and shortage of raw materials need to be considered,further improvement of the model is an important direction for future research.
作者
沈春娅
方辽辽
彭来湖
梁汇江
戴宁
汝欣
SHEN Chunya;FANG Liaoliao;PENG Laihu;LIANG Huijiang;DAI Ning;RU Xin(School of Mechanical Engineering,Zhejiang University,Hangzhou,Zhejiang 310058,China;Key Laboratory of Modern Textile Machinery&Technology of Zhejiang Province,Zhejiang Sci-Tech University,Hangzhou,Zhejiang 310018,China;Zhejiang Kangli Automatic Control Technology Co.,Ltd.,Shaoxing,Zhejiang 312500,China)
出处
《纺织学报》
EI
CAS
CSCD
北大核心
2024年第3期81-86,共6页
Journal of Textile Research
基金
浙江省博士后科研项目特别资助项目(ZJ2020004)
浙江省科技计划项目(2022C01202)
浙江省公益技术研究计划项目(LGG21E050024)
浙江理工大学科研启动基金项目(18022224-Y)。
关键词
织造车间
整经排产
模拟退火
多目标优化
约束优化
生产效率
weaving workshop
warping scheduling
simulated annealing
multi-objective optimization
constrained optimization
production efficiency
作者简介
第一作者:沈春娅(1993-),女,博士。主要研究方向为智能纺织装备技术;通信作者:汝欣(1989-),女,讲师。主要研究方向为纺织装备技术及智能制造。E-mail:ruxin@zstu.edu.cn。
