Based On Improved Genetic Algorithm Study On The Mixed Shop Scheduling Problem

concurrent engineering, agile manufacturing, virtual manufacturing, network-based manufacturing as a modern business such as advanced manufacturing model, the aim is to produce the lowest cost product of customer satisfaction. In these manufacturing model of how to use our limited resources, lower production costs, reduce the manufacturing cycle to ensure on-time delivery, improve credibility, win more customers, a reasonable method of scheduling constraints and optimization technology to achieve these objectives key factors, which shop scheduling problems are more and more attention by scholars.Genetic Algorithm (Genetic Algoirthm, GA) is a kind of learn from biological natural selection and natural genetic mechanisms of random search algorithm, which according to the optimization model of patience is not strong, to solve the problem of simplicity and robustness of the characteristics of widely used in all areas of manufacturing. In this paper, genetic algorithm on the basis of improvements around the issue of mixed shop scheduling is studied.In this paper, the following work: The article reviews and summarizes the workshop production of an overview of the development of scheduling problems, for clues to the genetic algorithm to scheduling problem of manufacturing systems and related issues as the background on the scheduling problem of the genetic algorithm and its math-related issues model; first of all, for a variety of job-shop JIT process workpiece line scheduling problem, taking into account the production process is affected by many factors, the coordination of a multi-tiered strategy objectives, the establishment of a flexible multi-objective function model, the hybrid genetic algorithm with the rugby Long day of relaxation algorithms based on a hybrid algorithm using genetic algorithm to update Lagrange multipliers of the optimal solution has been the problem, to verify the simulation model and method was feasible and practical; followed by targeted a mixture of a variety of flexible process routes the smallest completion time flow shop problem, combined with the production process planning and shop scheduling system, the principle of integration, the establishment of the target model, by a simple genetic algorithm to improve the algorithm to study the improved genetic algorithm (SGA) and simulated annealing algorithm (SA) combination of algorithms to optimize the integration of complementary mechanisms and structures, the formation of a more efficient hybrid optimization algorithm for solving the problem, specific examples are given to verify the effectiveness of algorithms and advanced sexual. Another method of combining object-oriented, technology-based components and threads, an application designed to optimize the actual production scheduling system modules, introduced the multi-level scheduling system is based on B/ S structure of the system structure, the business logic of the system made a detailed description of the production scheduling management system of the database development process; the end of this article the next step to improve the genetic algorithm based on a mixture of workshop production scheduling work to be carried out in future.