Solving Hybrid Flow-Shop Scheduling Based On Multi-Objective Artificial Bee Colony Algorithm

In order to improve the production efficiency of enterprises,we need to improve optimization method based on existing ones and find a more perfect production scheduling plan.Compared with the single objective shop scheduling,a multi-objective shop scheduling can describe complex production process in the enterprise more accurately.So improving existing optimization algorithm to solve multi-objective scheduling problem research has a certain practical value.As a species of intelligent algorithm,artificial colony algorithm has been used widely with its simple operation,less control parameters and robust.But it still has trouble when the problem became more complex such as the algorithm falling into local optimum easier and having a rapid convergence.As a common example,this paper come up with a multi-objective scheduling model for the hybrid flow shop scheduling problem of the production scheduling using the improved artificial bee colony algorithm to improve the performance of the solution.The main work includes:Firstly,in the process of artificial bee colony algorithm import the concept of multi-objective solution set.Discussed in the case of multiple objective expression of solution and evaluation standard,we put it into the artificial colony algorithm,reset the algorithm process.Because the artificial colony algorithm having a flexible and fast searching scope,we design adaptive search factor for different roles in the neighborhood search.In individual species selection,the paper comes up with an elite reserve strategy.Finally,choosing the optimal solution using the method of external files,it can keep excellent individuals to the greatest extent.Secondly,according to the characteristic of hybrid flow shop scheduling problem,choosing unrelated parallel machine as an example we design model of the problem.Combined with the improved artificial colony algorithm,problem can adapt to the demand of multi-objective.In view of the traditional coding method to produce the initial population diversity is an insufficient,decoding problem such as complex,we designed vector encoding and decoding based on each artifact initial processing.Improved the neighborhood search method combined with the concept of Pareto optimal solutions,it can make the algorithm more adaptive hybrid flow shop scheduling solution in the form of expression.By improving the population selection and elimination mechanism,we improve the probability of the searching the optimal solution with the multi-objective artificial colony algorithm,and improve the efficiency of the algorithm.Finally,using typical instances of scheduling algorithm for simulation test,through analyzing the algorithm to evaluate,the results show that multi-objective artificial colonyalgorithm global efficiency is higher,at the same time they also show a strong diversity of population distribution.Scheduling for steel-making-continuous casting production,for example,this paper designs some function for a system,simulates the actual production key process and can propose a scheduling scheme for the user.