Research On Hybrid Flow Shop Scheduling Problem Considering Learning Effect And Sequence-Dependent Setup Time

As a crucial part of the national economy,how the manufacturing industry can promote company production efficiency and lower production energy consumption to adapt to the increasingly fierce market competition environment has become a main problem it faces.Workshop scheduling,as the core part of manufacturing production management,is the key to solving this difficult problem.As a classic problem in workshop scheduling,the hybird flow shop scheduling problem is closely related to the production environment and production characteristics of the actual production workshop of the enterprise.It is diffusely used in machinery,semiconductor,textile and other manufacturing industries.Effective solutions to this type of problem can promote the company production capacity and lower production energy consumption.Production capacity of companises and lower production energy consumption.In previous studies,on the one hand,due to the high complexity of HFSP,scholars often do not consider the setup time or add it to the processing time.However,when the adjustment time is large enough,the error of the scheduling scheme will be caused if the adjustment time is not considered separately.On the other hand,scholars often assume that the processing time and adjustment time of the workpiece are fixed,but this assumption is not completely consistent with the realistic production situation,because the learning factor will influence the processing time and adjustment time of the workpiece,and will influence the production efficiency of the company.Based on this,in order to offer policy support and consulation for companies to formulate more reasonable production scheduling scheme,the paper deeply investigates the hybird flow shop scheduling problem considering learning factor and sequence-related adjustment time.First of all,based on the consideration of equipment energy consumption and resource limitation,this paper takes completion time and total energy consumption as target objects for coordination optimization,and establishes a multi-objective hybird flow shop scheduling problem model considering learning effect and sequence-related setup time.Secondly,as a new type of optimization algorithm,hybrid leapfrog algorithm has been diffusely used to settle different combined optimization problems due to its few parameter variables and simple operation.However,it still sinks into local optimum when settling some problems.In order to arrange the machining sequence of the workpiece and select the machining machine rationally to promote the production efficiency and lower the production energy consumption,an effective improved hybrid frog jump algorithm is proposed in this paper.In this algorithm,the two-layer coding method is adopted,and based on the idea of traditional hybrid leapfrog algorithm,the crossover principle is introduced in the local search for batch operation and the optimal individual for perturbation operation,which makes up for the defect that the algorithm is liable to be get caught by the local optimal and promotes the performance of the algorithm.And then,so as to test the rationality of the improved algorithm,the paper a smallscope case and a large-scope cas to the proposed multi-objective model,the algorithm offered in the paper,SFLA,NSGA-II,and SPEA-II are used to settle the cases and selected comprehensive performance index(IGD)and distribution index(C)are used as evaluation indexes,the algorithm offerded in the paper is contrasted and analyzed with SFLA,NSGA-II and SPEA-II,the event certify that the modified algorithm offered in the paper has better optimization ability.Then,the algorithm offered in this paper is used to settle the existing real examples,which further verifies the better performance of the algorithm,and analyzes the impact of learning effect on the hybird flow shop scheduling.