Improved Artificial Bee Colony Algorithm And Its Application In Flexible Job Shop Scheduling

Flexible job-shop is a meeting place for high-level materials,ordering tasks,and production feedback.Research on flexible job-shop scheduling and optimization is critical to the realization of intelligent manufacturing.The structure of artificial bee colony algorithm is simple and robust,which is suitable for solving NP hard optimization problems of job-shop scheduling.Based on the above background,this paper studies the improved artificial bee colony algorithm and its application in flexible job-shop scheduling.The main contents are as follows.Firstly,a flexible-job shop optimized single-objective scheduling model with maximum completion time is established,and propose a variable-step size artificial bee colony algorithm.This algorithm introduces a search threshold to obtain a variable step search strategy that combines large and small steps.Increase the number of detective bees to maintain the diversity of population.The effectiveness of the improved strategy is verified by a standard example on Kacem data set.Compared with the existing algorithms,the results show that the proposed algorithm has stronger optimization ability and convergence.Secondly,with maximum completion time,bottleneck machine load and total machine load as optimization objectives,the single-objective flexible job-shop scheduling is extended to a multi-objective problem.Inspired by the influence of the retention solution strategy on the search direction of the algorithm,two different population update strategies are designed,and a two-stage hybrid artificial bee colony algorithm is proposed.The first stage uses an independent update strategy to maintain the solution decentralization.The second stage uses a greedy strategy to retain new populations and accelerate the convergence of the algorithm.An improved inverse-order variation method with strong global search ability is proposed to improve the diversity of the populations using a multiple variation strategy.The effectiveness of the proposed algorithm is verified using 10 cases from the Brandimarte data set.Compared with existing algorithms,the proposed algorithm has better diversity and convergence and is applicable to flexible job-shop problems of different sizes.Finally,on the basis of the traditional production scheduling objectives,the ecological index of carbon emission is brought into the scheduling system.At the same time,considering the complex environment of the practical production and the dynamic disturbances,the stability is also incorporated into the optimization goal,and a dynamic scheduling model is constructed.An improved multi-objective artificial bee colony algorithm is proposed,which incorporates a heuristic variation method for the carbon emission target and combines a multiple variation strategy to improve the population diversity.Simulations are conducted to verify the fast response capability of the proposed algorithm,which reduces the impact of dynamic events on the workshop production,maintains the stable and efficient operation of the workshop.