计算机科学
渡线
作业车间调度
遗传程序设计
调度(生产过程)
流水车间调度
动态优先级调度
树(集合论)
分布式计算
布线(电子设计自动化)
数学优化
人工智能
操作系统
数学分析
计算机网络
地铁列车时刻表
数学
作者
Fangfang Zhang,Yi Mei,Mengjie Zhang
标识
DOI:10.1007/978-3-030-03991-2_43
摘要
Flexible job shop scheduling (FJSS) can be regarded as an optimization problem in production scheduling that captures practical and challenging issues in real-world scheduling tasks such as order picking in manufacturing and cloud computing. Given a set of machines and jobs, FJSS aims to determine which machine to process a particular job (by routing rule) and which job will be chosen to process next by a particular machine (by sequencing rule). In addition, dynamic changes are unavoidable in the real-world applications. These features lead to difficulties in real-time scheduling. Genetic programming (GP) is well-known for the flexibility of its representation and tree-based GP is widely and typically used to evolve priority functions for different decisions. However, a key issue for the tree-based representation is how it can capture both the routing and sequencing rules simultaneously. To address this issue, we proposed to use multi-tree GP (MTGP) to evolve both routing and sequencing rules together. In order to enhance the performance of MTGP algorithm, a novel tree swapping crossover operator is proposed and embedded into MTGP. The results suggest that the multi-tree representation can achieve much better performance with smaller rules and less training time than cooperative co-evolution for GP in solving dynamic FJSS problems. Furthermore, the proposed tree swapping crossover operator can greatly improve the performance of MTGP.
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