基于混合遗传鲸鱼优化算法的柔性作业车间自动导引车融合调度方法

针对柔性作业车间调度问题，考虑自动导引车(AGV)在车间制造过程中只参与装卸和搬运工作，提出一种实现AGV路径规划与柔性作业车间调度集成优化的融合调度模型。采用基于工序排序与机器选择两个子问题的二维向量编码方案，并在解码过程中提出基于最先服务原则的AGV安排策略。对鲸鱼优化算法进行离散化改进，针对性地设计了多种种群初始化策略，引入遗传算法的交叉、变异操作以提升鲸鱼优化算法的全局搜索能力，并嵌入局部搜索算法以达到全局搜索和局部搜索的平衡，构建了一种混合遗传鲸鱼优化算法（HGWOA）来求解该融合调度模型。通过经典测试算例验证了算法性能，并使用正交试验优化了算法参数。研究结果表明，HGWOA算法用于求解柔性作业车间AGV融合调度问题可以获得较好的效果。

Flexible Job Shop AGV Fusion Scheduling Method Based on HGWOA

Considering the AGV only participated in loading-unloading and transport in production processes of flexible job shop scheduling problem, an integrated scheduling model was proposed to ensure the integrated optimization of AGV path planning and flexible job shop scheduling. Then a two-dimensional vector coding scheme was applied based on two sub-problems of process sequencing and machine selection, and an AGV arrangement strategy was proposed based on the principle of first service in the decoding processes. The whale optimization algorithm was discretized, and a variety of targeted population initialization strategies were designed. The crossover operations and mutation operations of genetic algorithm were introduced to improve global search capability of whale optimization algorithm. The local search algorithm was embedded to achieve the balance between global search and local search. Then, a HGWOA was built up to solve the fusion scheduling model. The performance of the algorithm was verified by classical test examples. The parameters of the algorithm were optimized by orthogonal tests. Results show that HGWOA may achieve better results in solving AGV fusion scheduling problem of flexible job shops.