Research Of Workshop Scheduling Based On Improved Artificial Bee Colony Algorithm

Production scheduling refers to maximizing production costs,production efficiency,and resource utilization through reasonable resource allocation and scheduling.In modern industrial and commercial fields,production scheduling is an indispensable and important link,among which workshop scheduling,as the core lifeline of industrial production,plays a decisive role in factory production efficiency and enterprise income generation.By optimizing workshop scheduling,it is possible to maximize the utilization of production resources,improve production efficiency,reduce costs,and enhance the profitability and market competitiveness of enterprises.Therefore,the solution of workshop scheduling problems is crucial for the development of enterprises.At the same time,the application fields of workshop scheduling problems are very extensive,covering various aspects from industry to commerce,so its research and application have important practical significance and application prospects.Artificial Bee Colony(ABC)is an emerging biomimetic algorithm based on the relationship between bee populations and honey sources in nature.ABC improves the efficiency of solving complex scheduling problems through the mutual cooperation between bee colonies and the mutual transformation between bee species.This paper studies the foraging behavior of honeybee population based on ABC algorithm.By combining the mutation idea of genetic algorithm(GA),and combining the advantages of NEH heuristic algorithm(Nawaz Enscore Ham,NEH)and Tabu search algorithm(TS),two improvements are made to ABC algorithm,The improved algorithms will be applied to solve Flexible Flow Shop Scheduling(FFSP)and Hybrid Constrained Flow Shop Scheduling(HCFSP)respectively.The main research content of this article is as follows:Firstly,a Gaussian Cauchy Artificial Bee Colony(GCABC)algorithm combined with genetic algorithm was proposed.By adding Gaussian and Cauchy perturbation factors,the algorithm is improved in two stages: hiring bees and observing bees.Modeling and solving the flexible flow shop scheduling problem with the goal of minimizing the maximum completion time.Experiments were conducted on two datasets of different sizes to compare the improved algorithm with GA algorithm and ABC algorithm,verifying that the improved algorithm improves convergence speed and enhances the ability to obtain global optimal solutions.Secondly,a hybrid artificial bee colony algorithm(NTABC)was proposed.The algorithm uses the NEH heuristic algorithm to generate high-quality initial solutions,and integrates the Tabu search algorithm in the bee detection phase.Through the Tabu search strategy,it jumps out of the local optimum,increases the diversity of the population,and improves the convergence speed and accuracy of the algorithm.Modeling and solving the mixed constraint flow shop scheduling problem with the goal of minimizing the maximum completion time.Experiments were conducted on four different datasets to compare the improved algorithm with TS algorithm and ABC algorithm,verifying the effectiveness of the algorithm improvement.This article proposes two improved algorithms based on ABC,which are respectively applied to solve two types of scheduling problems.Simulation experiments have shown that both improved algorithms have achieved satisfactory results in terms of solution accuracy and convergence speed.The research results of this article contribute to the development of artificial bee colony algorithms and production scheduling theory.