The Study On Improved Ant Colony Algorithm Meeting The Demand Of Production Scheduling In Flexible Manufacturing

The market competition not only promotes the technology development of manufacturing, but also promotes the change of the management mode of manufacturing enterprises. Since the 1970s, the MRP (Material Requirement Plan) occurred, the production management model changed greatly, and now we have entered the ear of flexible manufacturing. The flexible manufacturing adapts the production systems to the market demands by the way of reforming the system structure, organization, operation and marketing, in order to get greater business benefits. However, flexible manufacturing model brought tremendous challenges to the production and management. It abolishes the constraints to the chain process route and the only once processing in the equipment in the flexible Job-Shop Scheduling Problem. This makes the complexity grow rapidly, so how to scheduling effectively has become a problem that the enterprises have to face.Production scheduling system must obtain all of the information of the Enterprise Manufacturing Resource for effective scheduling, to maximize the business interests, but because of the tremendous data and a wide range of distribution under the environment of flexible manufacturing technology, we use the productive resources snapshot and production scheduling snapshot to define production scheduling system using the smallest data set, combine the theory and the reality of the production scheduling problem as the start line and the end of production scheduling research and at the same time, it is an isolation between production scheduling research and the complex business environmentDue to the complexity of production resource structure and the difference between the function and properties, coupled in varying degrees, so if we directly use the real productive resources as the object of production scheduling, it will greatly increase the complexity of scheduling, and may expand the scale with a geometric progression. Therefore, this article will abstract all kinds of properties of productive resources, establish the abstract productive resources with the same characteristics, and reduce the complexity on the study of production scheduling. Then it adjusts abstract productive resources from the performance and function, establish rate merger, chip rate of sub-slice, function combination and function choice, this four types of virtual work, so that the properties of productive resources adapts production scheduling, to reduce the size of the model.The impact on the business interests is multifaceted. The merits of production scheduling results should include orders, production costs, inventory costs and other series of comprehensive evaluations. Therefore in this article, we establish a comprehensive evaluation system taking a means of cost accounting as the evaluation criteria of enterprise production scheduling results and maximize the benefits of the business.Ant Colony Optimization Algorithm is a stochastic global optimization search algorithm, with parallelism, robustness and global search, etc., it has been widely used in a variety of complex combinatorial optimization problems solving including the production scheduling problem and has gotten good results. However, the existing ant colony algorithm can only bound the chain process line in the production scheduling research, so the findings can not be used for products that are assembled by a number of independent components, limiting the scope of production scheduling research and application. Therefore this paper establishes ant colony algorithm of the three-dimensional in-tree model to solve the problem of complex production process line scheduling problem, enhances the application capabilities in production scheduling and introduce the Production Scheduling process under the flexible manufacturing environment of using ant colony algorithm of the three-dimensional in-tree model in detail.Finally, we design and develop the improved ant colony algorithm to verify the system and using three-dimensional model ant colony algorithm to solve the effectiveness of the production scheduling problem under the flexible manufacturing environment which is multiple objectives optimization, complex process line, expected work center downtime. At the same time, using the common FT20 example to verify model can solve the Job-Shop problem. And this preliminarily verifies the correctness of this article.