Research on Flexible Job-Shop Scheduling Problem Based on the Dragonfly Algorithm

Abstract

An effective scheduling scheme plays an important role in the rapid development of modern manufacturing. In order to reduce the cost of production and improve economic profit, it is essential to minimize the makespan for production tasks. This paper proposes a flexible job-shop scheduling problem based on the Dragonfly Algorithm (FJSP-DA) to solve the makespan minimization in the flexible job-shop scheduling problem(FJSP). The Dragonfly Algorithm(DA) has been proven to have good optimization performance and better convergence. But DA is more suitable for the continuous optimization problem, while FJSP is a discrete optimization problem. To make DA applicable to the FJSP, we introduce two-layer coding and position vector correction into the FJSP-DA model. And aiming at the problem that DA is easy to fall into local optimum, we use the roulette wheel selection to remove the individuals with poor fitness and regenerate the population if the optimal value of the population does not change for a certain number of iterations. Finally, the performance of the proposed DA in solving the FJSP is compared with the Particle Swarm Optimization algorithm (PSO). And simulation experiments demonstrate the efficiency and feasibility of FJSP-DA.