Research On Intelligent Scheduling Algorithm Of Mixed-flow Operation In Multi-variety And Small-batch Mode

With the development of modern manufacturing industry,customers tend to be more individualized and more diverse in customization,production scheduling managers are increasingly demanding the standardization and refinement of workshop operation efficiency and production models,therefor,more and more companies are begining to adopt mixed-flow operations of multi-variety and small-batch in order to optimize the production scheduling,shorten the production cycle,reduce production costs and increase corporate profits.However,during the production process,many unexpected situations often occur,such as: emergency order insertion,machine failure,processing delay and so on,which not only seriously interfere with normal production order,but also increase the uncertainty of the workshop operation.Based on the common evaluation index of flexible manufacturing workshop,a static scheduling model of mixed-flow shop of multi-variety and small-batch with the target of the maximum completion time,the total load of the machine tool,the maximum processing cost and the maximum single machine load is proposed in this dissertation.Through transforming the three common disturbance factors of machine failure,emergency order insertion and regular order insertion into the constraints of the model,a dynamic scheduling model that more suitable for the actual situation of the workshop scheduling is obtained.In order to solve the multi-objective scheduling model of workshop,this dissertation provides the Improved Multi-target Cuckoo Search Algorithm(IMOCS),which adaptively adjusts some parameters and Levi's flight steps,adds a bidirectional search strategy,and introduces the idea of simulated annealing.In addition,this dissertation uses a bi-level programming coding method,Multi-objective Cuckoo Search Algorithm(MOCS),Improved Multi-objective Cuckoo Search Algorithm,Non-dominated Sorting Genetic Algorithm(NSGAII)to solve the dynamic scheduling model,evaluates the three intelligent optimization algorithms in terms of convergence efficiency,selects an optimization algorithm that is more suitable for a four-dimensional dynamic scheduling model,proves the feasibility of the improved algorithm.After using intelligent algorithm to solve the shop scheduling model,this dissertation uses Analytic Hierarchy Process(AHP)to establish a judgment matrix,through calculation,the weight values of the four optimization objectives are obtained,optimal processing sequence and machine sequencing are selected in Pareto solution set.Finally,this dissertation taking the scheduling information of a multi-variety mixed-flow production enterprise as an example,through calculation and comparison of results,proves the rationality of the dynamic scheduling model of the workshop and the superiority of the Improved Multi-target Cuckoo Search Algorithm.