EDA And Drosophila Algorithms Solve The Distributed And Complex Parallel Machine Scheduling Problem

With the coming of Industry 4.0,the factory production model based on distributed local small-scale rapid design and production has been applied to many industries;in this paper,distributed shop scheduling has also become a research hotspot in the field of scheduling.The intelligent algorithm can obtain the approximate optimal solution of the distributed complex parallel machine scheduling problem in a short time.Therefore,it is of great significance to solve this kind of problem by using the distribution estimation algorithm and the fruit fly algorithm.In this paper,the distributed estimation algorithm and the fruit fly algorithm and its improved algorithm are used to solve three important distributed complex parallel machine scheduling problems.The main task is as follows:(1): For the distributed multi-parallel machine scheduling problem(DMPMSP),a hybrid distribution estimation algorithm is designed to minimize the maximum completion time of the considered problem.In the algorithm,firstly,the population is initialized by random generation,which effectively guarantees the diversity of the initial population.Secondly,the next generation is generated by updating the probability matrix,and the iterative direction is effectively guaranteed by learning the optimal individual position information;Finally,local search operations based on multiple mechanisms allow an effective balance between global search and local search.Simulation experiments and algorithm comparisons verify the effectiveness and robustness of the designed hybrid Estimation of Distribution Algorithm.(2): Based on the actual production problems faced in industrial production,and based on(1),the scheduling problem model of distributed heterogeneous parallel machine is proposed.Then,a hybrid fruit fly optimization algorithm is designed to minimize the maximum completion time of the considered problem.Firstly,the competition mechanism is added in the initialization phase of the algorithm,which effectively improves the quality of the initial solution;Secondly,the adaptive search radius is introduced in the smell search stage to effectively search the solution space;Finally,the three-phase local search is integrated into the update phase of the algorithm,so that global search and local search can achieve a better balance.Simulation experiments and algorithm comparisons verify the effectiveness and robustness of the designed hybrid fruit fly optimization algorithm.(3): On the basis of(2),A distributed heterogeneous parallel machine scheduling model with assembly line is proposed.A hybrid fruit fly optimization algorithm is designed to minimize the maximum completion time of the considered problem.In the algorithm,Firstly,by randomly generating the initial population,the diversity of the initial population is effectively guaranteed;Secondly,the adaptive search radius is introduced in the smell search stage to effectively search the solution space;Finally,in the local search phase,domain search based on multiple mechanisms is incorporated,which improves the ability of local search.Simulation experiments and algorithm comparisons verify the effectiveness and robustness of the designed hybrid fruit fly optimization algorithm.