Research On Ant Colony Optimization Algorithm For Flexible Job-Shop Scheduling

Flexible Job-Shop Scheduling problem is a more complex combinatorial optimization problem.In this problem,the processing of the process is no longer limited to one machine,but it can be performed on multiple devices.The processing times are different each other.In addition to determining the sequence of the operations,it is also arranging the machine for the processes,which is called one of the worst NP-hard problems.Compared with the classic job shop scheduling problem,it is more in line with the actual production needs,so the majority of scholars pay more and more attention to the method of solving Flexible Job-Shop scheduling problem.In this paper,the ant colony algorithm is used as the optimization algorithm,and the optimization goal is to discuss the flexible job shop scheduling with the minimum maximum completion time.The main work of this paper is to improve the ant colony algorithm state transition rule and pheromone update strategy.Apply in the flexible job shop problem and give experimental results and conclusions.(1)Based on the analysis of machine selection problems,combined with the characteristics and difficulties of machine selection.a machine selection strategy is proposed.The combination of probability distribution and machine load is used to select the processing machine for the process.(2)Due to the shortcomings of long search time.lack of initial pheromone and easy to fall into local optimal solution for ant colony algorithm in solving flexible job shop scheduling problems,an improved ant colony algorithm is proposed to optimize the flexible job shop scheduling problem.In the following four aspects:for the shortcomings of the initial pheromone deficiency of the ant colony algoritim,the pheromone is initialized using a genetic algorithm.The genetic algorithm uses the trncation mechanism to select the iritial population,selects the frne individuals to cross-rnutate,selects the nodes using the pseudo-random proportion rule of the prior knowledge selection path and probability formula search,and dynamically sets the fixed parameters in the ant colony system.Using a combination of local pheromone and global pheromone update to update pheromone.select a processing procedure to locally update the pheromone,and then only update the pheromone on the optimal path globally after once iteration.After the completion of the iteration,the genetic algoritlun's mutation mechanism was introduced to mutate the optimal path and improve the ant's global search ability.(3)The proposed improved ant colony algorithm is used in the design of flexible job shop scheduling problems.The steps are designed,including key modules such as machine selection and operation sequencing.The specific flow is given,the parameters in the algorithm are determined,and the improved algorithm is implemented through MATLAB programming.,simulate multiple cases and compare the results with other algorithms to verify the efficiency and feasibility of the improved algorithm.(4)Taking the scheduling problem of a certain furniture company's workshop as a practical case,it is solved and compared with the basic ant colony algorithm to verify the feasibility and efficiency of the irmproved ant colony algorithm.