Abstract:

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.

Key words: weaving workshop, warping scheduling, simulated annealing, multi-objective optimization, constrained optimization, production efficiency