The Application Research On Intelligent Computing In The Flexible Job-shop Scheduling Problem

The shop scheduling is the core of enterprise management,it is very important to research on effective scheduling and optimization in the competitive society,so as to improve productivity and reduce cost.Nowadyas,more and more researchers have paid attention to this research and this problem has achieved fruitful results in theoretically after more than half a century of research.Flexible Job-shop Scheduling Scheduling problem is an extension of the workshop Scheduling Problem,its characteristics more in line with the actual production process,but also the difficulty in solving exist problem at the same time,therefore,to realize the ddecisin-making process in job distribution and time through proper use of optimization algorithm is considered valuable in both theiry and practice.In recent years,many intelligent algorithms are applied to this kind of problem.Because of the fast convergence rate,simple in algorithm model,less parameter configuration,strong robustness,easy to control and easy to be understood,the BA has also applied to Flexible Job-shop Scheduling Scheduling problem.In this paper,two kinds of improved bat algorithms have proposed after research on shop problem.Its purpose is finding out the ways to solve this problem in theory and practice,and provides the basis of further improve and development of the relevant technical.So this paper use improved bat algorithm to solve FJSP.Researching work includes mainly following content: 1)Introduced the research background and research purpose and has carried on a literature review;2)Summarized the research atatus on JSP and FJSP at home and abroad;3)Introduced the biological mechanism of bat algorithm and the algorithm flow,then discussed and illustrated relevant algorithm improved the research progress and application status.Moreover,according to the advantages,disadvantages and application scope,analyzed and compared Bat – inspired Algorithm,genetic algorithm and particle swarm optimization algorithm.Finally,summarized the existing problems in research on Bat – inspired Algorithm and suggested some future research directions to solve the problems;4)Proposed two new bat algorithm improvement methods,An improved adaptive hybrid bat algorithm(YSBA),with gaussian mutation and Levy flight bat algorithm(MGBA),the experi Mental results show that the two algorithm is superior than BA in solve the problems of trapped into local search and has high calculation accuracy;5)Established the mathematical model respectively for the traditional job-shop scheduling problem and flexible job-shop scheduling problem,using MGBA to solve FJSP.