关键词: 运筹学, 作业排序问题, 启发式Q学习, 汽车涂装车间, 涂色批量

Abstract: Aiming at the optimization of job scheduling in automotive paint shops, an optimization algorithm based on heuristic Q learning is proposed. For starters, a multi-objective integer programming model is established with the purpose of keeping painting sequence consistent with the production sequence of the assembly shop and minimizing the number of color switches. The optimization problem of painting job is abstracted into a Markov process, and a solution method based on heuristic Q algorithm is established. Through the specific cases, the advantages and disadvantages of heuristic Q learning, Q learning and genetic algorithm are compared and analyzed. The results show that the heuristic Q learning algorithm has the advantages of higher efficiency and better effect, which are more pronounced in large-scale issues. This study proposes a new idea for the application of machine learning algorithms in the optimization of job scheduling in automotive paint shops.

Key words: operations research, car sequencing problem, heuristic Q-learning, automotive paint shop, color-batch