Flexible Job Shop Scheduling Based On Hybrid Artificial Bee Colony Algorith

With the advent of industrial manufacturing 4.0,the manufacturing system is changing to the direction of multiple varieties and small batches,and the production mode of flexible job shop is widely used.Artificial Bee Colony Algorithm(ABC)has been widely concerned since it was proposed because of its advantages of flexible mechanism and few parameters.It is very suitable for designing hybrid algorithms with other operators to solve various problems.In this paper,ABC is combined with a variety of excellent operators and strategies to design different hybrid ABC algorithms,focusing on the difficulties and priorities of three flexible job shop scheduling problems(FJSP),and solving them respectively.The main work of this paper is as follows:(1)According to classical flexible job-shop scheduling problem,to minimize the maximum completion time(makespan)for scheduling goal,single objective flexible job-shop scheduling model is set up,and put forward a hybrid rules initialization,greed decoding and hybrid artificial colony algorithm of the neighborhood structure,enhanced algorithm in solving the problem of single objective scheduling in local development ability and stability.The effectiveness of the improved strategy is verified by a BRdata instance.The experimental results show that the algorithm proposed in this chapter can obtain smaller Makespan scheduling scheme.(2)On the basis of the single-objective scheduling research,a multi-objective flexible job shop scheduling model was established with makespan,total machine load and maximum machine load as objectives,and a hybrid artificial bee colony algorithm based on load balance was proposed.The algorithm is improved in neighborhood search strategy,heuristic search based on load balance and multi-rule scouter bee mechanism,so that the algorithm has better convergence and distribution.The effectiveness of the proposed strategy is verified by a BRdata instance.Finally,the proposed algorithm is compared with three classical algorithms,and the results show that the proposed algorithm can provide more and better scheduling schemes for decision makers.(3)Considering the uncertainties existing in actual production,a multi-objective fuzzy flexible job shop scheduling model is established based on fuzzy mathematics theory,and a hybrid artificial bee colony algorithm based on zonation and divide is proposed.In order to solve the problem that Pareto dominance relation cannot accurately describe fuzzy multiobjective relation,the algorithm first designs individual dominance relation judgment with fuzzy number index value.Based on the analysis of the general laws existing in the evolution process of the population,the evolution characteristics were extracted,and the population was divided into five regions according to the evolution characteristics.The divide-and-conquer strategy was adopted to conduct targeted search for each region,so as to reduce useless search and improve the overall efficiency of the algorithm.The effectiveness of the improved strategy is verified by comparison of multi-objective evaluation indexes on Lei and Wang.Finally,5algorithms are selected and compared with the algorithm in this chapter in the above two instances to verify that the proposed algorithm has better convergence and diversity in uncertain scheduling environment.