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• W4387005498 abstract "Dynamic flexible job shop scheduling (DFJSS) is a critical and challenging combinatorial optimisation problem. Genetic programming (GP) has been widely used to learn scheduling heuristics for DFJSS automatically. Ideally, we prefer to have effective and small scheduling heuristics which tend to be easy to be interpreted. However, the effectiveness and the sizes of scheduling heuristics are conflicting, and reducing the rule sizes tends to worsen the effectiveness of scheduling heuristics. This is a typical multi-objective optimisation problem. However, the existing studies in multi-objective DFJSS consider the effectiveness-related objectives only such as minimising max-flowtime and mean-flowtime rather than interpretability-related objectives such as rule size. To fill this gap, this paper aims to propose an interpretability-aware multi-objective GP to learn a well-distributed Pareto-front of scheduling heuristics for DFJSS. This paper first adopts a multi-objective GP algorithm with α-dominance and archive from a routing problem to DFJSS. Then, we propose to use traditional dominance relation based Pareto front for α and archive updating and an objective normalisation based α-dominance sorting strategy to further improve the performance of the adopted multi-objective GP. The results show that the proposed algorithm can obtain significantly better performance than the state-of-the-art multi-objective GP algorithms in different DFJSS scenarios." @default.
• W4387005498 created "2023-09-26" @default.
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• W4387005498 date "2023-07-01" @default.
• W4387005498 modified "2023-10-16" @default.
• W4387005498 title "Interpretability-Aware Multi-Objective Genetic Programming for Scheduling Heuristics Learning in Dynamic Flexible Job Shop Scheduling" @default.
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• W4387005498 doi "https://doi.org/10.1109/cec53210.2023.10253970" @default.
• W4387005498 hasPublicationYear "2023" @default.
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