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

Created by W.Langdon from gp-bibliography.bib Revision:1.8612

@InProceedings{Zhang:2018:AJCAImt,

• author = "Fangfang Zhang and Yi Mei and Mengjie Zhang",
• title = "Genetic Programming with Multi-tree Representation for Dynamic Flexible Job Shop Scheduling",
• booktitle = "Australasian Joint Conference on Artificial Intelligence",
• year = "2018",
• editor = "Tanja Mitrovic and Bing Xue and Xiaodong Li",
• volume = "11320",
• series = "LNCS",
• pages = "472--484",
• address = "Wellington, New Zealand",
• month = dec # " 11-14",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Multi-tree representation Flexible job shop scheduling Dynamic changes",
• isbn13 = "978-3-030-03990-5",
• URL = "http://link.springer.com/chapter/10.1007/978-3-030-03991-2_43",
• DOI = "10.1007/978-3-030-03991-2_43",
• abstract = "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.",
• notes = "conf/ausai/ZhangMZ18",

}

Genetic Programming entries for Fangfang Zhang Yi Mei Mengjie Zhang

Citations