Genetic Programming with Multi-tree Representation for Dynamic Flexible Job Shop Scheduling

• Published in: AI 2018: Advances in Artificial Intelligence Vol. 11320; pp. 472 - 484
• Main Authors: Zhang, Fangfang, Mei, Yi, Zhang, Mengjie
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Dynamic changes; Flexible job shop scheduling; Genetic programming; Multi-tree representation
• ISBN: 9783030039905; 3030039900
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-03991-2_43

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.