Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 51, Issue 4, Pages 1797-1811Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2020.3024849
Keywords
Feature extraction; Dynamic scheduling; Task analysis; Job shop scheduling; Sequential analysis; Genetic programming; Heuristic algorithms; Dynamic flexible job-shop scheduling (DFJSS); feature selection; genetic programming (GP); hyperheuristics; interpretability
Categories
Funding
- Marsden Fund of New Zealand Government [VUW1509, VUW1614]
- Science for Technological Innovation Challenge Fund [E3603/2903]
- MBIE SSIF Fund [VUW RTVU1914]
- China Scholarship Council/Victoria University Scholarship
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A novel two-stage GPHH framework with feature selection is designed in this article to automatically evolve scheduling heuristics in DFJSS, and individual adaptation strategies are proposed to utilize information. Results show that the proposed algorithm can successfully achieve more interpretable scheduling heuristics with fewer unique features and smaller sizes, and reach comparable scheduling heuristic quality with much shorter training time than traditional algorithms.
Dynamic flexible job-shop scheduling (DFJSS) is a challenging combinational optimization problem that takes the dynamic environment into account. Genetic programming hyperheuristics (GPHH) have been widely used to evolve scheduling heuristics for job-shop scheduling. A proper selection of the terminal set is a critical factor for the success of GPHH. However, there is a wide range of features that can capture different characteristics of the job-shop state. Moreover, the importance of a feature is unclear from one scenario to another. The irrelevant and redundant features may lead to performance limitations. Feature selection is an important task to select relevant and complementary features. However, little work has considered feature selection in GPHH for DFJSS. In this article, a novel two-stage GPHH framework with feature selection is designed to evolve scheduling heuristics only with the selected features for DFJSS automatically. Meanwhile, individual adaptation strategies are proposed to utilize the information of both the selected features and the investigated individuals during the feature selection process. The results show that the proposed algorithm can successfully achieve more interpretable scheduling heuristics with fewer unique features and smaller sizes. In addition, the proposed algorithm can reach comparable scheduling heuristic quality with much shorter training time.
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