Research On Flexible Job-shop Scheduling Problem Based On Multi-objective Optimal Algorithms

In today's society,energy consumption has attracted more and more global attention.Flexible job-shop scheduling is also facing the dual task of reducing energy consumption and improving production efficiency.In practical research,flexible job-shop scheduling problem usually has more than one objective to optimize,and needs to consider multiple performance indicators at the same time.Flexible job-shop scheduling problem is taken as the research object,and multiple flexible job-shop scheduling constraints are considered.With the objective of minimizing maximum completion time and minimizing maximum energy consumption,the mathematical model of flexible job-shop scheduling problem is established.The improved multi-objective optimization algorithm is applied to the study of flexible job-shop scheduling.The optimal solution set of scheduling problem is obtained to reduce energy consumption and production efficiency.The main tasks are as follows:1.A multi-objective optimization algorithm FNSGA based on improved NSGA-II algorithm is proposed.The algorithm is used to solve the problem that traditional multiobjective optimization algorithm is slow in searching and low in scheduling efficiency when solving flexible job-shop scheduling problem.It is mainly form the following aspect:(1)NSGA-II algorithm and particle swarm algorithm are combined to effectively optimize parameters and prevent the algorithm from falling into local optimization;(2)The initialization operation is improved to ensure the diversity and legitimacy of solution set.(3)The strategy of elite selection is improved to retain the better individuals and improve the population level,and the effect of deleting individuals on the crowding density of neighborhood individuals is considered.The traditional multi-objective optimization algorithm of FNSGA is compared with the traditional multi-objective optimization algorithm in the standard test data set of flexible job-shop scheduling problem.The results show that FNSGA is superior to the traditional multi-objective optimization algorithm in terms of running speed,convergence of the optimal solution set and individual diversity,and decisionmakers can obtain more effective decision-making schemes2.An improved multi-objective grasshopper optimization algorithm IMOGOA is proposed.Multi-objective grasshopper parameter optimization algorithm is simple,easy tooperate,with the useage of large space,but also exist algorithm run unstable and falling into the most superior shortcomings,which is mainly improved from the following aspect:(1)A cosine adaptive parameters instead of the grasshopper of linear adaptive optimization algorithm is introduced to improve the global search ability and stability of the algorithm;(2)The archiving technology is applied to the save selection process of Pareto solution set.By comparing IMOGOA and traditional multi-objective grasshopper optimization algorithm,NSGA-?algorithm in standard test data set and analyzing the same and different energy consumption when the machine processes different workpieces in different processes,the results show that the IMOGOA algorithm is superior to the unimproved multi-objective optimization algorithm in terms of stability and convergence.