The Engineering Optimization Of Mold Storage System

As an important part of the national economy, the degree of automation in themold industry determines the production efficiency. With the continuousimprovement of the modern enterprise production scale, the level of automationstorage systems has become a big problem obviously. This paper carry out theresearch based on the mold warehouse storage as the research background, combiningmodern intelligent optimization theory in the field of engineering, in order to improvethe operating efficiency and the intelligent management level.Automatic warehouse system itself has a dynamic, randomized, multi-objective,discrete, and many other special attributes. With these complex characteristics, if usethe traditional method, the process is long and very difficult to get the optimalsolution. This thesis uses the genetic algorithm to carry on the warehouse jobmanagement, path selection and route planning, optimization problems.Genetic algorithm simulates the evolution process of biological population. Theessence of genetic algorithm is a kind of search method to analyzing efficientlyglobally. It can use the known information in the search process efficiently. It canaccumulate or obtain information about the search space automatic. Search parameterscan be constrained so that it eventually tends to be optimal solutions. Encoding thepath based on storage location and scheduling list, this thesis has designed manyheredity operator, including the population initialization, the selection, crossover, andmutation and so on. It is also to be considered in the relevant parameters for theresearch and planning.Research shows that, through the reasonable modeling, effective scheduling, thenthe optimization technology can well solve the mold storage in various constraints inscheduling and assignment problem, to realize the automation of storage andscheduling, efficient and reasonable. This thesis adopts genetic algorithm in solvingthe problem of system optimization, storage on practice shows that, by using thegenetic algorithm can improve the scientific planning level of the storage system,improve production efficiency. Genetic algorithm is an excellent algorithm forcombinatorial optimization problems.