Optimization And Application Of Job Scheduling Problem Based On Improved Genetic Algorithm

With the rapid development of industrialization,enterprises are unable to meet production needs only by relying on internal high-tech equipment resources.Network resource sharing is imminent.Effective scheduling strategy can improve the utilization ratio of resources,reduce the cost of equipment,shorten the waiting time of users,and improve the efficiency of the whole system.considering the different starting points of their own interests,the enterprises pay more attention to the production efficiency.the users are more concerned about the time and cost of processing operations,and the different procedures of job can be carried out in different stages on different machines.Therefore,considering the above aspects,the job scheduling method has become the key factor of equipment resource management and allocation,and balancing multiple objectives.Aiming at the problem of multi-user reservation equipment in the environment of equipment resource sharing,The mathematical model of equipment resource scheduling is constructed,and the shortest time required for the equipment to complete the operation and the lowest cost of consumption are taken as the optimization target of the model.In order to solve the model effectively,genetic algorithm is used as the basic algorithm framework,and two improved genetic algorithms are designed for optimizing problem model.The study is divided into two kinds of situations.(1)For the situation of the job submitted by each user only needs to be processed on one equipment,this paper proposes a scheduling strategy based on the crowding mechanism genetic algorithm.the heuristic crossover which is beneficial to the growth of individual optimal model,and the mutation operation and the crowding mechanism which maintain the diversity of the population are added to the improved genetic algorithm,which enhances the searching ability and optimization ability of the algorithm.(2)For the situation of the job submitted by each user include multiple processes,which need to be processed on multiple machines,In the process of solving,it is necessary to consider the starting processing time for each job and the processing sequence between the processes,and the expectations of different users.This paper designs an initial solution generation method based on greedy idea and active decoding mechanism,and proposes an improved non dominated sorting genetic algorithm.The algorithm uses the fast non dominant sorting algorithm and the elite selection strategy to speed up the convergence speed of the algorithm.At the same time,the hierarchical clustering operator in the intensity Pareto algorithm is introduced to select the required individual at the same level,and maintain the diversity of the Pareto frontiers.The algorithm obtains the non dominated solution set by iterative optimization,and then obtains the optimal scheduling strategy of the problem by AHP.In this paper,the large-scale instrument service platform system is mainly to realize the sharing of scientific and technological equipment resources.Due to the development of the Internet,the number of online users is increasing.When there are more users booking equipment at the same time,there is a problem of resource conflict,and the requirements of different users are different.Therefore,the server side job scheduling scheme,on the one hand,affects the efficiency of network equipment resources,and on the other hand,it also affects user satisfaction.Aiming at this problem,this paper adopts the improved non dominated sorting genetic algorithm proposed in this paper to help users generate scheduling schemes satisfying multiple constraints and multi objectives.