Study Of Inteligent Algorithm For Optimized Bathcing Problem

In the course of casting production, the batching is very important steps. Under the current fierce market competitive environment, present craft experience batching method can't already meet the requirement of reduce the production cost and strengthen the need of enterprise's synthesized competitiveness. So for solve current craft experience batching method's disadvantage, applying optimization technique to study a more efficient and more convenient method becomes important approach and certain measure.Firstly, this paper has enumerated the commonly used batching method in the course of preparing burden at present; analyzes the shortcomings of these methods, points out that the need for the use of intelligent algorithm. According to the process and method of modeling, this paper studies the optimized batching problem, then set up the math model.Secondly, this paper analyses the principle and characteristics of genetic algorithm, points out the key points of Genetic Algorithm (GA). Combining with the property of the optimized batching problem, this paper designs the genetic operator of selection, cross, mutation, proposes the method of solving the optimized batching problem with GA.Again, this paper analysis of the characteristics of Simulated Annealing Algorithm (SA), proposes the method of solving optimized batching problem with SA and solves the key problems: the selection of initial temperature; state produce function and state update function, proposes the method of solving the optimized batching problem with SA.Finally, the method solving optimized batching problem based on GA and SA is applied in actual data as experiment. The experiment result shows that both of them get better result than craft experience batching method, which could minimum the cost while Meeting the ingredients constraint. The experiment result indicates that the solution is efficiency in practical application, which proofs validity of the model and feasibility of the method.