Research And Application Of Workshop Scheduling Problem Based On Firefly Algorithm

Production scheduling is a key link to improve resource utilization and enterprise efficiency,it is a process of arranging production resources according to production schedule to meet constraints and optimize performance indicators,it plays a key role in the whole manufacturing system and has a wide range of applications,including industry,commerce and so on.Job shop scheduling problem is the key part of the whole production scheduling and the main way for enterprises to allocate resources,manufacturing enterprises have been exploring the optimization scheme of job shop scheduling.Firefly algorithm（FA）is based on the modeling of fireflies transmitting information between peers,it is a new swarm intelligence optimization algorithm.FA mainly uses the changes of brightness and attraction to iterate the position to complete the optimization process,and has good results in solving various scheduling problems.This paper mainly studies and improves FA algorithm and applies the improved algorithm to function optimization and flow shop scheduling（FSP）.The main research contents of this paper are as follows:（1）This paper explains the research background and significance of flow shop scheduling problem,and summarizes the research on the improvement and application of intelligent optimization algorithm by scholars at home and abroad.（2）A parallel firefly algorithm combined with genetic operator（PFAGO）is proposed,the adaptive Gaussian mutation mechanism with different variance is introduced,the population searches the solution space with decentralized mutation scale,maintains the population diversity,reduces the premature probability;and evolves in parallel to improve the speed and quality of solution.Function experiments show that PFAGO algorithm improves the global search performance and can find a more optimal solution.（3）A multi-scale cooperative mutation hybrid firefly particle swarm optimization algorithm（HFPMCV）is proposed,based on the hybrid firefly particle swarm optimization algorithm（FAPSOMA）,the dynamic adaptive strategy is introduced to divide the population into two groups,and the two groups evolve in parallel,so as to maintain the population diversity and improve the solution speed at the same time;The multi-scale Gaussian mutation operator is introduced to further improve the local retrieval ability of the algorithm by initializing the population through chaos.The test function shows that HFPMCV algorithm can achieve better results.（4）PFAGO and HFPMCV algorithms are applied to FSP,taking the completion time as the index and using TA scheduling set simulation,it is verified that the improved algorithm can get a better scheme.This topic mainly makes two improvements to FA algorithm,the feasibility of the improved algorithm is verified by function test,and the scheduling experiment verifies that the improved algorithm has better practicability.The research of this paper is of great significance to accelerate the progress of evolutionary algorithm and production scheduling theory.