Research On Mixing Equipment Scheduling Based-on Improved Genetic Algorithm

As an important type of production scheduling, Flow-shop production scheduling is widely used in the production and operation of manufacturing enterprise. With increasing exquisite of production process and technology of manufacturing enterprises, hybrid production of automatic and manual equipment become a common status. Therefore a research of the automatic and manual equipment mixed Flow-shop (AMEMFS) scheduling problem has an important practical significance.Firstly, a brief introduction of the standard Flow-shop scheduling problem is provided. And based on the summary analysis of the current research status of Flow-shop scheduling problem, the necessity and practical significance of the research of AMEMFS scheduling problem is expounded. Secondly, a further study of AMEMFS scheduling problem which is the set of the processing penalty cost of non-working time under the consideration of manual equipment working cycle characteristics result of the alternating of work time and non-working time of manual equipment operators is provided. The issues of increment time and processing of non-working time within the model construct of AMEMFS scheduling problem are elaborated. Then, an improved genetic algorithm (IGA) is developed based on the AMEMFS scheduling problem. IGA has its unique coding method based on the advantage and disadvantage of genes. And IGA uses the inner traversal of chromosome to replace the chromosomal crossover operation to improve certainty of populations breeding. Finally, dual-machine and multi-machine hybrid Flow-shop scheduling problem are researched in this paper to further analysis of AMEMFS scheduling problem. The strategy of processing starting increment time and processing of non-working time are both analyzed. Based on these, both dual-machine and multi-machine hybrid Flow-shop scheduling model are constructed and IGA is implemented to solve these models. The simulation experiment data show the superiority of IGA on solving AMEMFS scheduling problem.