- author = "Fangfang Zhang and Yi Mei and Su Nguyen and Mengjie Zhang",
- journal = "IEEE Transactions on Cybernetics",
- title = "Multitask Multiobjective Genetic Programming for Automated Scheduling Heuristic Learning in Dynamic Flexible Job-Shop Scheduling",
- year = "2023",
- volume = "53",
- number = "7",
- pages = "4473--4486",
- keywords = "genetic algorithms, genetic programming, Dynamic jobshop scheduling, GP, hyperheuristics, multiobjective, multitask learning",
- ISSN = "2168-2267",
-
URL = "
https://meiyi1986.github.io/publication/zhang-2022-multitask/",
-
DOI = "
doi:10.1109/TCYB.2022.3196887",
- size = "14 pages",
- abstract = "Evolutionary multitask multiobjective learning has been widely used for handling more than one multiobjective task simultaneously. However, it is rarely used in dynamic combinatorial optimization problems, which have valuable practical applications such as dynamic flexible job-shop scheduling (DFJSS) in manufacturing. Genetic programming (GP), as a popular hyperheuristic approach, has been used to learn scheduling heuristics for generating schedules for multitask single-objective DFJSS only. Searching in the heuristic space with GP is more difficult than in the solution space, since a small change on heuristics can lead to ineffective or even infeasible solutions. Multiobjective DFJSS is more challenging than single DFJSS, since a scheduling heuristic needs to cope with multiple objectives. To tackle this challenge, we first propose a multipopulation-based multitask multiobjective GP algorithm to preserve the quality of the learned scheduling heuristics for each task.",
-
notes = "also known as \cite{9868257}
Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand",
