Research On Textile Workshop Scheduling Problem Based On Swarm Intelligence Optimization Algorithm

Textile workshop belongs to mixed process production,which has the characteristics of continuous and discrete.The production process has the characteristics of relying on raw materials,multi equipment parallel processing,long process flow,frequent product switching and so on.However,most of the existing production scheduling methods in textile workshops rely on human experience,which is not only time-consuming and labor-consuming,but also has low utilization rate of production equipment.In addition,nowadays,orders show the characteristics of multiple varieties,multiple batches and few batches,which makes the efficiency of manual production scheduling extremely low.In order to make enterprise production better adapt to today's market demand and improve economic efficiency,this paper mainly does the following work:(1)The production process of YD textile workshop under complex conditions is studied and analyzed.The textile workshop scheduling problem is described as a hybrid flow shop scheduling problem with parallel machines.This paper discusses the workshop production mode,points out the shortcomings of the original fixed production line mode relying on mass order production,and puts forward a new production mode.Considering the constraints of order weight,machinery and equipment,production process,etc.a bi-objective scheduling model with the minimum makespan and minimum variety switching times is established by adding variety switching times to optimize the completion time.(2)This paper proposes an improved gray wolf optimization algorithm.The improved strategies are the hunting search strategy based on dimension learning,the nonlinear convergence parameter strategy and the variable weight search strategy.The improved gray wolf algorithm is simulated with the original gray wolf algorithm,artificial bee colony algorithm,whale optimization algorithm and sparrow search algorithm in the classic Taillard flow shop test examples.The results show that the improved gray wolf algorithm has better ability of optimization,which makes it have higher precision and good robustness in complex search space.(3)The improved grey wolf optimization algorithm is used to solve YD textile workshop scheduling problem in fixed production line and unfixed production line.For the double objective problem,the step-by-step priority weighting method is used to transform it into a single objective problem which is easy to solve.Through the case simulation and experimental comparison,it is verified that the improved gray wolf algorithm has higher convergence accuracy and stability than the contrast algorithm,whether in the fixed production line or the non fixed production line production mode,and can get a better scheduling scheme in the non fixed production line mode.The above experiments show that the improved grey wolf optimization algorithm can well solve the hybrid flow shop scheduling problem in complex applications,and can realize the optimal scheduling in large-scale textile production workshop with the production mode of non fixed production line,which has practical significance.