Multi-Objective Optimization Of Job Shop Scheduling And System-Developing Under Hybrid Genetic Algorithm

With the globalization of production and markets, the21st century manufacturing industry is facing increasingly fierce global market competition; the era of knowledge economy is impacting the tide of production and operation activities. To the fierce competition in place, and companies must be customer-centric, the original production as the center of production to a customer-centric mode of production. Therefore, manufacturing companies must strive to achieve T, Q, C, S, E the comprehensive development of the accurate delivery time (Time), quality (Quality), cost (Cost), a full range of services (Services), focusing on environmental protection (Environment), which is business management and production process monitoring requirements are also increasing. How in the fierce competition in the market to ensure the production of efficient and stable operation; how to co-ordinate arrangements and existing human and material resources to achieve maximum profit, every business has been exploring and study questions.Production scheduling task is to meet the production equipment and workpiece machining processes premise, according to market demand rational allocation and utilization of existing resources to complete a variety of production targets and optimize the system as much as possible the various properties for the enterprise zone to significant economic benefits. Production scheduling objective is to form a detailed schedule task scheduling scheme, the production process planning and control. In this paper, double-resource job shop manufacturing process as the research object, the most commonly used mode of production enterprises under the production process modeling and production scheduling decisions were studied.Firstly, the production decisions and the status of shop scheduling problems are described, and research issues related to the theory, methods are analyzed.Then, this paper shop scheduling problem of the production process has been studied, elaborated for the problem formalization Petri net modeling tools. On the basic theory of Petri nets, application methods conducted in-depth research. Shop scheduling problems due to the complexity of ordinary Petri net modeling for scheduling problems would be very complicated model, which not only makes modeling difficult, and to the system analysis and control caused great obstacles. In response to this problem, this paper introduces object-oriented colored timed Petri net model, which will Petri Nets.coloring. The abstraction mechanisms and object-oriented classes, inheritance concept of integration, will be simplified as much Petri nets subnet, greatly simplifies the complexity of the model, the model is improved degree of reuse. In this paper, colored timed Petri net modeling theory for a structured, more complex production process modeling. Modeling results model is simple, as this follow-shop scheduling genetic algorithm optimization provided.Secondly, this paper improved genetic algorithm theory studied. Niche technology combined with improved genetic algorithm in solving shop scheduling problem solving speed and effectiveness.Then, the double resource scheduling problem, a multi-objective job shop, improved genetic algorithm, multi objective genetic algorithm for solving job shop scheduling problem of the Pareto set.Finally, this paper will produce optimal decision theory, methods and modern information technology, the use of ASP. NET and SQL Server technology, the application of genetic algorithm shop scheduling problem solving, and decision-making for the production, production scheduling optimization decisions main content developed and implemented a production decision support system. This study closely the development needs of manufacturing companies, for the actual production process modeling, and model application based on improved genetic algorithm pairs resources, multi-objective job shop scheduling problem, and get a more optimized solution. On this basis, developed and implemented on a production decision support system to achieve a good aforementioned research and scientific decision-making to improve the level of production, economic efficiency and market competitiveness has a good practical significance and reference.