Research On Uncertain Production Scheduling Based On Improved Quantum Genetic Algorithm

As the most important link in production management, a better production scheduling method can improve the capacity and efficiency for production. Since the fifties, academics have done the massive research to classic production scheduling, and it has achieved remarkable result. However, in the actual production process, there are lots of uncertain factors that make scheduling scheme not working properly. Therefore, the analysis of the uncertain factors in production process, which will be introduced to the mathematical model of production scheduling, is necessary. Studying an effective optimization algorithm for the problem has important theoretical value and practical significance.The thesis introduces some basic situation of production scheduling, and emphatically summarizes the research status of the production scheduling in the conditions of uncertainty, and then leads to the advantages of the quantum genetic algorithm by comparing the strengths and weaknesses of several optimization algorithms; secondly, the uncertain factors in production process are analyzed in detail, and the appropriate treatments are given, and then the fuzzy mathematical model of uncertain production scheduling is established; thirdly, the quantum genetic algorithm is introduced, including its special encoding, the principle of population update by quantum rotation gates, and its characteristics is analyzed, then the author proposed several local operators, the steps of the algorithm are also given out; At last, the algorithm is used to solve the uncertain production scheduling.At present, there are some main optimization algorithms used in uncertain production scheduling, such as genetic algorithm, immune algorithm, ant colony algorithm and so on, they all can solve the question effectively, but it need to spent a lot of time in the actual optimization process. According to characteristics of time in uncertain production process, this thesis proposes a new quantum genetic algorithm. It has more efficient parallel computing capabilities because of the parallel characteristics in the quantum computing. It also has better population diversity for its quantum bit coding, so the population size can be very small but it don't affect algorithm, this paper also gives several local operators to improve algorithm partial searching ability, an then jump out of local optimum well. The improved quantum genetic algorithm will be used in uncertain production scheduling, and the simulation results show that the improved algorithm can achieves the desired effect effectively.