The Research And Appliance Of Genetic Algorithm In Solving The Workshop Scheduling Problem

Workshop scheduling is an important content in modern scheduling problem. It means to achieve a best goal of the distribution which suffices the common resource to fit time request in the manufacture processes. There are many decision-making Optimizations in the scheduling problem; qua one of necessary part, a nicer workshop scheduling system has an important significance to improve product efficiency. Scheduling system is not only reducing vacancy and cost of equipment but also shorten lifecycle of product and enhancing benefit. Based on technical route, workshop scheduling problem divide into two parts: job shop scheduling and flow shop scheduling problem. Among the large-scale manufacturing enterprise, there exists many shops, and therefore this paper has both theoretical and practical value.As a NP-hard problem, the job shop scheduling and flow shop scheduling problem will increase exponentially along with the adding of job and machine numbers. So it becomes an important studying trend to solve the workshop scheduling problem with genetic algorithm. On the basis of building the model of job shop scheduling and flow shop scheduling problem, this paper studies to solve the problem with genetic algorithm.Focusing on workshop scheduling problem, this paper mainly includes the next contents:(1) This paper analyses the model of job shop scheduling problem, based on this analysis and combines especially example of job shop scheduling problem, this paper introduces the model of flow shop scheduling problem solving with genetic algorithm;(2) This paper adopts genetic algorithm to solve job shop scheduling problem. At the same time comparing the two problems, this paper introduces the each model of job shop scheduling and flow shop scheduling problem solving the genetic algorithm.(3) Also this paper analyses the function of genetic algorithm, and improves the genetic algorithm based on simple genetic algorithm.(4) Do research on the models introduced by this paper, and analyses the simple genetic algorithm and modified genetic algorithm.