Swarm Intelligence Algorithm Of Job-shop Scheduling Problems

Production scheduling problem is a typical scheduling problem.It plays an important role in many systems,and has the very high value of theoretical research and practical application.In essence,production scheduling problem is the combinatorial optimization problem in discrete domain,and is proved to be NP-hard.Job-shop scheduling problem is one of those with more constraints and difficulty.The traditional analysis modeling and optimization methods could not cater to the demand.There are also deficiencies in solving the problems with swarm intelligence algorithm and the improved one.The large discrete solution space and relation between each operation make job shop scheduling problem complex.The whole solution structure would change with one adjusted.Therefore,to solve the job shop scheduling problem,the global and local search abilities of swarm intelligence algorithm would interact and effect together.The paper put emphasis on analyzing two kinds of swarm intelligence algorithms with outstanding solving ability,particle swarm optimization algorithm and artificial fish swarm algorithm,to deal with job-shop scheduling problem.And then some improvements has been made on the original algorithms,in order to improve abilities of the global search and jumping out of local extreme value.Finally,the effectiveness of the two improved algorithms are verified with the simulation results.