Dynamic flexible job shop scheduling (JSS) is an important combinatorial optimization problem with complex routing and sequencing decisions under dynamic environments. Genetic programming (GP), as a hyperheuristic approach, has been successfully applied to evolve scheduling heuristics for JSS. However, its training process is time consuming, and it faces the retraining problem once the characteristics of job shop scenarios vary. It is known that multitask learning is a promising paradigm for solving multiple tasks simultaneously by sharing knowledge among the tasks. To improve the training efficiency and effectiveness, this article proposes a novel surrogate-assisted evolutionary multitask algorithm via GP to share useful knowledge between different scheduling tasks. Specifically, we employ the phenotypic characterization for measuring the behaviors of scheduling rules and building a surrogate for each task accordingly. The built surrogates are used not only to improve the efficiency of solving each single task but also for knowledge transfer in multitask learning with a large number of promising individuals. The results show that the proposed algorithm can significantly improve the quality of scheduling heuristics for all scenarios. In addition, the proposed algorithm manages to solve multiple tasks collaboratively in terms of the evolved scheduling heuristics for different tasks in a multitask scenario.

中文翻译：

---

用于动态灵活作业车间调度的代理辅助进化多任务遗传编程

动态柔性作业车间调度（JSS）是一个重要的组合优化问题，在动态环境下具有复杂的路由和排序决策。遗传编程 (GP) 作为一种超启发式方法，已成功应用于 JSS 的调度启发式演化。但是，它的训练过程非常耗时，并且一旦工作车间场景的特征发生变化，它就会面临重新训练的问题。众所周知，多任务学习是通过在任务之间共享知识来同时解决多个任务的有前途的范式。为了提高训练效率和有效性，本文提出了一种新的代理辅助进化多任务算法，通过 GP 在不同的调度任务之间共享有用的知识。具体来说，我们使用表型表征来测量调度规则的行为并相应地为每个任务构建代理。构建的代理不仅用于提高解决每个单个任务的效率，而且还用于在具有大量有前途的个体的多任务学习中进行知识转移。结果表明，所提出的算法可以显着提高所有场景下调度启发式的质量。此外，所提出的算法根据多任务场景中不同任务的进化调度启发式，设法协作解决多个任务。构建的代理不仅用于提高解决每个单个任务的效率，而且还用于在具有大量有前途的个体的多任务学习中进行知识转移。结果表明，所提出的算法可以显着提高所有场景下调度启发式的质量。此外，所提出的算法根据多任务场景中不同任务的进化调度启发式，设法协作解决多个任务。构建的代理不仅用于提高解决每个单个任务的效率，还用于在具有大量有前途的个体的多任务学习中进行知识转移。结果表明，所提出的算法可以显着提高所有场景下调度启发式的质量。此外，所提出的算法根据多任务场景中不同任务的进化调度启发式，设法协作解决多个任务。