Green Multi-objective Flexible Job Shop Scheduling Problem Based On Improved Genetic Algorithm

As the foundation of our country and the foundation of a strong country,the manufacturing industry has been vigorously developed in recent years.The scale has gradually changed from the original production of single variety and single production line to flexible production of multiple varieties and multiple production lines.With the increase of product types and processes,traditional shop scheduling has gradually failed to adapt to the current production situation.Flexible job shop scheduling has gradually become the main scheduling mode in manufacturing industry.Because it does not immobilize the machining sequence and machine of the workpiece,it is more suitable for the actual production situation.Because of the flexibility of flexible job shop processing,how to reasonably allocate the processing sequence and processing machine is more and more important for the production efficiency of enterprises.Therefore,in order to better schedule flexible manufacturing processes for enterprises,this paper adopts genetic algorithm(GA)and Non-dominated Sorting Genetic Algorithm II(NSGA-II)derived from it to study Green Flexible Job Shop Scheduling Problem(GFJSP),and the main results of this paper are as follows:(1)An Improved Genetic Algorithm(IGA)was proposed for the problem of flexible job shop scheduling with the optimization target of the maximum completion time.Specific improvement methods include:design a double-layer coding scheme based on machine coding and process coding,design three initialization methods to obtain the initial population,The initial population solution’s quality was improved by the iterative process.Greedy algorithm is introduced to obtain a new feasible solution through the destruction of the original feasible solution and subsequent reconstruction.With the Variable Neighborhood Search Algorithm(VNS),Designing three distinct types of local structures to augment the locality is the aim.Finally,different algorithms are compared to verify the efficiency of the improved genetic algorithm.(2)Aiming at flexible job shop scheduling problem with maximum completion time and total power as optimization objectives,an Improved Non-dominated Sorting Genetic Algorithms II(INSGA-II)was proposed.Specific algorithm improvements include: using fast non-dominated sequencing to achieve fast sequencing of chromosome quality,adding multi-population co-evolution,and retaining possible high-quality genes through the re-evolutionary operation of eliminated individuals.Finally,Verifying the efficacy of the augmented algorithm is accomplished by contrasting various algorithms.(3)Aiming at flexible job-shop scheduling problem with Makespan and total power as optimization objectives,a new NSGA-II combining niche technology and learning mechanism was proposed considering intermachine transportation time.Through the introduction of niche technology,combined with the selection of inferior species,cub protection and other evolutionary methods,the search ability is strengthened,the learning mechanism is introduced,and the yield of progeny individual is improved.Finally,different algorithms are compared to verify the efficiency of the improved genetic algorithm.