An Immune Genetic Algorithm For Solving Multi-Objective Flexible Job-shop Scheduling Problem With Low Carbon

Flexible manufacturing and green manufacturing are the main line in the planning of made in China 2025,and also the key to the transformation and upgrading of manufacturing enterprises.Because the multi-objective flexible job shop scheduling problem is the most compatible with the flexible production environment,it has gradually become the focus of the researches on the shop floor scheduling,so it is of great theoretical and practical significance to study the multi-obj ective flexible j ob shop low carbon scheduling problem.In this paper,a hybrid immune genetic algorithm is used to study the modeling and solution of low carbon scheduling problem in multi?objective flexible job shop.First,based on the analysis of the machining process,a formula for calculating the energy consumption of the machine is established,which combines the unloaded power and the processing time.Then on the basis of the traditional flexible job-shop scheduling model,new constraints are added,and from three aspects of production efficiency,equipment utilization,energy resources to establish a new type of scheduling model with the minimum makespan,the bottleneck machine load the smallest,the target of minimizing the total energy consumption.Second,the immune genetic algorithm(MG-IGXA)based on memory guidance is proposed to solve the scheduling model by combining the immune algorithm and genetic algorithm.The immune algorithm is suitable for comprehensive coarse grain search,but the search precision is poor.In the genetic algorithm,the genetic manipulation of chromosomes can be carefully searched,but it is easy to get into local optimum.Combining the two algorithms can realize complementary advantages,and the immune algorithm can make up for the shortage of local convergence of the genetic algorithm,and the genetic algorithm can improve the search precision of the immune algorithm.MG-IGXA includes genetic algorithm module(GA),memory library module(ML)and immune algorithm module(IA).GA module,combined with the maximum heuristic rule based on work piece residual processing time and the initial population of random method,improved the initial solution quality;A plurality of genetic manipulations are used to optimize the population.In the cross operation,the similarity threshold is used to introduce the elite solution of the memory bank and guide the direction of population optimization.The ML module uses the memory library to save the elite solution generated by each iteration,so as to avoid losing the elite solution and leading to the degradation of the algorithm.In the IA module,the affinity,similarity threshold and individual concentration are calculated based on the distance of the hamming distance,and redundant individuals are eliminated according to the similarity threshold to prevent the generation of redundant information from generation.The new antibody was generated in the same way as the initial population,and the similarity determines whether the new antibody is added to the population to ensure the diversity of the population.The population was then trimmed and distributed evenly.In the end,the power is divided into two classes,between 0-1 and 1-5,to avoid the impact of the numerical disparity of power.the paper uses 26 benchmark examples of ntwo kinds of power to test the quality and distribution of the solution.Each group of examples runs 20 times in a row,received 520 groups experiments.The experiment shows that the proposed algorithm can obtain Pareto solution with better quality and more uniform distribution,which fully verifies the feasibility and effectiveness of the model and algorithm.