The Research On Intelligent Scheduling Of Production Planning Based On Quantum Genetic Algorithm

The production process of textile industry is very complicated, and the automation of machine is not enough. So the management of production is mostly dependent on the experienced managers and dispatcher. Furthermore, the product of textile is various and the order of textile enterprises is excessive, so the production planning of shop is difficult to achieve. Traditionally, the production planning is made by hand. Because of the inefficiency and inaccuracy of the manual production planning, the delivery date of orders is hard to achieve. To satisfy the demand of modern rapid production and improve the competitiveness of textile enterprise, the intelligent production planning system is applied to optimizing the production planning. We describe a certain planning problem based on the spinning shop of YOUNGOR group, use the quantum genetic algorithm to solve it and improve the quantum genetic algorithm. Ultimately, we give the simulated result of the intelligent scheduling algorithm to optimize the production planning.Firstly, we studied the multi-objective permutation flow shop scheduling problem based on the quantum genetic algorithm. We applied the random weight multi-objective quantum genetic algorithm to minimize the makespan and the delaytime of permutation flow shop scheduling problem, increased the searching direction. The design approach of quantum genetic algorithm for multi-objective permutation flow shop scheduling problem is given, including the quantum coding, fitness function, the parameter of the algorithm, the quantum rotation, crossover and mutation operation, and the terminate condition. To illustrate it, we use the genetic algorithm and the quantum genetic algorithm to solve the same concrete example of the permutation flow shop scheduling problem. The simulation result indicates that the quantum genetic algorithm has many advantages such as the small popsize, simple operating and good optimal performance, comparing with the genetic algorithm.Secondly, the hybrid flow shop problem is studied, and a quantum genetic algorithm based on the separator number arrow is proposed. The coding method and the novel crossover operator is introduced. Because of the new coding method, the design of operation is simplified. Using the the quantum genetic algorithm and traditional genetic algorithm to optimize the same example of hybrid, the simulation results shows the superiority of the quantum genetic algorithm.Thirdly, according to the attributes of the textile enterprise, its planning problem is simplified to a problem similar to the hybrid flow shop problem, and the mathematic model is presented. Based on the model, the quantum genetic algorithm is used to simulate the production planning of spinning shop in YOUNGOR group. The calculation outcome demonstrates that the model and the method are effective.