Application Of Improved Bee Evolutionary Genetic Algorithm In Job Shop Scheduling

The manufacturing industry is one of the most important means to promote economic development and national technological progress, at the same time, it plays an irreplaceable role in the process of material wealth creation for the social life, therefore, the national economy and international competitive level can be seen from the strength of manufacturing industry, it can laid a solid foundation for the entire national economy, too. Production scheduling is the key for production management of the manufacturing process, an optimization scheduling of the realistic production largely determines whether the task of management could be successfully carried out and completed. So how to produce a realistic and reasonable production scheduling? There would be a good design and reasonable optimal scheduling algorithm to support the production scheduling. besides, the existing production scheduling systems put the equipment as the only resource constraint, but it is different from the actual existence, as there is the operator constraints, tool constraints, and other resource constraints in reality, the complexity of such scheduling was increased, some of the existing single production scheduling has been unable to meet the actual needs. This article exists to overcome the defects of the current scheduling, and designing a generic dynamic production scheduling system, then propose an improved and reasonable optimal scheduling algorithm.Over these years, genetic algorithms are applied to many fields, and have achieved inspiring results, scholars in various fields have ultimately concluded that genetic algorithm is fitted in global searching and other parameters in the optimization field, it has also got remarkable achievements in the job shop scheduling problem solving. Based on extensive literature of genetic algorithms and the bee evolutionary genetic algorithm, we propose one improved bee evolutionary genetic algorithm (IBEGA), it introduces the principles of bees breeding of the nature into the genetic algorithm, the algorithm structure is basically the same as conventional genetic algorithm, the self-adaptive selection operator is used to limit the size of the random population in each generation, And then, controlling operation that extends biodiversity of the swarm is introduced to avoid premature convergence. The improved bee evolutionary genetic algorithm are not only of the traditional benefits, but also has it's unique advantages of fast convergence, not easily fall into local optimum and so on, so it can be effectively applied to solve scheduling problems.Aimed at the actual problems of a factory, we introduced the above improved techniques into the actual production scheduling system, and the new algorithm was applied to the dynamic production optimization model for solving practical problems, the results were feasible and effective.