Application Of Improved DNA Immune Genetic Algorithms To Workshop Scheduling Model Base System

Scheduling theory belongs to combination optimize problem. How to resolve the dynamic scheduling problem in orer to make an product plan efficient is the primary problem in the scheduling research field. With development of market economy, many orders of multi-process and small-batch become the focus of the market which manufactory racing to acquire. In this way, manufactories should be asked to arrange sequences rationally, take advantage of resource, shorten time limit for a project and reduce cost of producing. Introducing the optimization theory to the field of job scheduling in workshop can improve the performance of algorithm, make the algorithm apply to broader fields and complete the whole system of the algorithm, which is a subject including both theoretical meaning and practical values.An artificial immune system is a new intelligence system based on the characteristics of biological immune system. According to the information processing mechanism of an immune system in biotic science, on the basis of simple genetic algorithm, we proposed a new DNA immune genetic algorithm for job shop scheduling through combining immune algorithm with DNA genetic algorithm. We built a genetic codon table, to decode DNA base chain. Information exchange between subgroups employs the island model in this algorithm. We can generate the initial population and keep the diversity of them with a cluster algorithm. We raise the fitness of an antibody by vaccination from a vaccine base, and prevent species degeneration by immune selection. These improved measures are of great significance on reducing complexities and enhancing convergence velocity, as well as increasing global searching ability of the algorithm. The improved algorithm is tested through using examples in the standard test set and the actual problem in scheduling. The simulation results of the test show that this algorithm can complete the task of the fast search in the given range and global optimization.In this thesis we study the scheduling theory and its development in a systemical way, and present a solution scheme which has complete theory, reliable practical foundation and high feasibility, to solve the production control problem in some manufactural enterprise, then we designed and implemented an intelligent scheduling model base system platform for workshop. Further studying many intelligent search algorithm model. The improved algorithm model is applied to actual problem and the result is feasible and effective.