The Research On The Two Stage Non-permutation Assembly Flow Shop Scheduling

With the development of social technology,the expansion of production scale,thecomplexity of production and assembly is getting higher and higher,and the competition in the market is getting bigger and bigger.Especially in today's personalized and diversified customer needs,in order to meet customer and needs,companies need to continuously improve manufacturing technology and improve management methods to enhance production efficiency and improve customer satisfaction with the company and the company.In the actual production scenario,a variety of different parts can be produced on different production lines and eventually assembled into different products.That is,the two-stage assembly flow shop,which is the flow shop environment frequently used by enterprises to improve efficiency and realize the matching of processing components and products,is the generalization of the environmental problems of the two-machine flow shop.First,each product component is processed in the first stage,on multiple machining lines,and then assembled on the second stage assembly machine to form the final product.In this process,the coordination of processing and assembly is particularly important.Although most of the flow shop research only considers permutation scheduling,this is not a strict restriction on manufacturing,but the simplicity of replacement scheduling.If you do not use the first-come,first-served principle to significantly improve the performance of the system,most companies will be willing to let go of the same processing order in the actual situation.Therefore,considering the whole system,the scheduling study of the two-stage non-permutation assembly flow shop is very meaningful.Based on the existing theory,this paper summarizes and analyzes the research status at home and abroad,and studies the non-permutation flow shop,two-stage assembly flow shop,and the combination of scheduling environment problems,dynamic programming,meta-heuristic,heuristic,etc.Commonly used solution algorithm.In the model,the components are processed separately on each sub-production line,and finally assembled on the assembly machine,and the same processing order is released,so that the model is more flexible and practical.Verifying the necessity of research in non-permutation flow shop by comparing with the permutation flowshop in the first stage.In order to more accurately reflectthe accuracy and satisfaction of customer service,and to minimize the total completion time of the jobs,an effective solution is proposed.In this scheduling environment,in order to ensure that the component production stage can provide the required components in the assembly machine in time and accurately,and the overall completion time is minimized,the two-stage assembly flow shop and the non-replacement flow shop scheduling method are studied.Firstly,the mathematical model of the two-stage non-permutation assembly flow shop was established,and the model was modified and verified using cplex software.In the aspect of problem solving,under the small data scale,based on the dynamic programming method,a specific dynamic programming model belonging to this problem is proposed,and the recursive relationship is given.Each phase includes two aspects: the original state expansion and the new state addition.The state enumerates all states of each phase.Next,by analyzing the problem attribute,the strategy of state space reduction is given from the aspects of non-permutation and two-stage assembly.The initial solution range is limited,the use of the boundary value,the assembly load constraint,etc.are verified and the status is improved.The current situation of huge space.Through the example,the state is enumerated in all cases,and the experimental data is compared with the results of cplex to prove the validity of the state space reduction strategy and the accuracy of the dynamic programming solution.Under the big data scale,an improved Tabu search algorithm is proposed and compared with the simulated annealing algorithm to prove the effectiveness of the improved algorithm.