Research On The Job-shop Scheduling Problem Based On The Improved Gcnctic Algorithm

Classical job-shop scheduling problems have had fruitful research findings, but these aremostly based on specific model that can’t be adapted to the complex dynamics of enterpriseproduction environment. This article studies in the flexible job-shop scheduling problem anddynamic scheduling problem based on the flexibility and dynamic characteristics of thescheduling problem, putting forward the Improved Genetic Algorithm optimization schedulingmethod. Flexible job-shop scheduling problem has an optional set of machine. It is the extensionto the classical job-shop scheduling problem by reducing the constraint conditions of themachine. Dynamic scheduling based on the static scheduling aims at the dynamic events thatdisrupt the initial scheduling to have a new scheduling. Therefore, results of the flexible job-shopscheduling and dynamic research have important theoretical significance and engineeringpractical significance.The main contents of the thesis include:The first chapter focuses on introducing the general situation of workshop scheduling,scheduling algorithm and so on at home and abroad after elaborating research background.Meanwhile, the research content and the framework of the paper are also presented in thischapter.The second chapter introduces the characteristics, classification and the description of theclassical job-shop scheduling problem. And the dynamic scheduling method and schedulingpolicies are emphatically introduced.The third chapter introduces the content and the basic principle of the Genetic Algorithm.Aiming at defects of the basic genetic algorithm, this paper introduces an improved adaptivegenetic algorithm, which has adaptive fitness function and adaptive genetic mechanism. Thealgorithm is verified by simulation experiments.The fourth chapter expounds the flexible JSP, and the mathematical modeling and objectivefunction. This paper introduces the double chromosomes of genetic coding method and thecorresponding genetic operators and designs simulation experiment to validate effectiveness ofthe improved genetic algorithm.The fifth chapter elaborates the description of flexible job-shop dynamic scheduling,scheduling goal and the objective function. Dynamic scheduling strategy is proposed based onimproved genetic algorithm. And two cases of emergency order insert and machine errors areanalyzed. Finally, simulation experiments on the algorithm and scheduling strategy verify the effectiveness.The sixth chapter is thesis summary and outlook of the research.