Research On Optimal Scheduling Of Hybrid Flow Shop With Batch Production Constraints

With the increasingly fierce competition in the international market,Germany creatively proposed the "Industry 4.0" model,and China successively proposed the"Made in China 2025" plan in 2015.Chinese manufacturing industry urgently needs transformation,changing traditional production methods and using information technology and digital technology to improve the efficiency and quality of production.Batch scheduling,as a typical scheduling of workshop scheduling,has a wide range of applications in actual industrial production.Batch processing machines are often used as the bottleneck process of the flow shop,which restricts the pace of enterprises’development.Therefore,the scheduling research on batch processing machines has important theoretical and practical significance for improving the efficiency of flow shop,accelerating enterprises’ transformation,realizing the "Made in China 2025" plan,and achieving a manufacturing powerhouse.In this thesis,the initial stage and any stage have serial batch machines,and a planning model is established for the problems.The improved genetic algorithm and the improved artificial bee colony algorithm are used to solve the problems.The specific research content is as follows:Firstly,the problem of hybrid flow shop scheduling with minimizing the maximum completion time is studied.The initial stage has serial batch machines,and the other stages are discrete machines.Considering the factor of transportation time,an integer programming model is established,and an improved genetic algorithm is proposed to solve it.In the algorithm,a random method is mixed with NEH heuristic algorithm to generate the initial population,and to ensure the diversity and quality of the population,Secondly,designing the probability formula of crossover and mutation to promote the evolution of the population.Finally,embedding multiple neighborhood structures to increase the diversity of solution space.Based on 360 sets of examples,the improved genetic algorithm is compared with the other two algorithms.The experimental results prove the effectiveness and accuracy of the algorithm.Through the initial stage of the serial batch processing machines research,the research content is further promoted,and it is extended to the case that any stage has serial batch processing machines.The model is built with the goal of minimizing makespan,and an improved artificial bee colony algorithm is designed.In the algorithm,the workpiece information is compiled into a two-dimensional matrix,on leading bee phase,using crossover and mutation to improve the search ability of the bee colony,on onlooker bee phase,designing three kinds of neighborhood structures to ensure the search ability,and to speed up the calculation of the algorithm during the destruction and reconstruction of the scount phase.Introduced 900 sets of orthogonal tests to investigate the impact of parameter indicators,and simulated small,medium,and large-scale examples.The experimental results show that the APRI value of the design algorithm is significantly smaller than the other three algorithms,and the accuracy of solution is better.This thesis studies the flow shop environment with serial batch processing machines at different stages.Considering the factors of unrelated machines and transportation time,two algorithms are designed to solve such problems.The feasibility of the algorithm is proved through experiments,which not only expands the series of the research in the field of batch processing machines bur also provides certain guidance for the production decision-making of enterprises.