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. Furthermore, we develop a multitask multiobjective GP algorithm with a task-oriented knowledge-sharing strategy to further improve the effectiveness of learning scheduling heuristics for DFJSS. The results show that the designed multipopulation-based GP algorithms, especially the one with the task-oriented knowledge-sharing strategy, can achieve good performance for all the examined tasks by maintaining the quality and diversity of individuals for corresponding tasks well. The learned Pareto fronts also show that the GP algorithm with task-oriented knowledge-sharing strategy can learn competitive scheduling heuristics for DFJSS on both of the objectives.

中文翻译：

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动态灵活车间调度中自动调度启发式学习的多任务多目标遗传编程


进化多任务多目标学习已广泛用于同时处理多个多目标任务。然而，它很少用于动态组合优化问题，而动态组合优化问题具有有价值的实际应用，例如制造中的动态灵活作业车间调度（DFJSS）。遗传编程（GP）作为一种流行的超启发式方法，已被用于学习调度启发式算法，仅用于生成多任务单目标 DFJSS 的调度。使用 GP 在启发式空间中搜索比在解决方案空间中搜索更困难，因为启发式的微小变化可能会导致无效甚至不可行的解决方案。多目标 DFJSS 比单个 DFJSS 更具挑战性，因为调度启发式需要处理多个目标。为了应对这一挑战，我们首先提出了一种基于多群体的多任务多目标 GP 算法，以保持每个任务的学习调度启发式的质量。此外，我们开发了一种具有面向任务的知识共享策略的多任务多目标 GP 算法，以进一步提高 DFJSS 学习调度启发式的有效性。结果表明，所设计的基于多群体的GP算法，特别是具有面向任务的知识共享策略的算法，通过很好地保持相应任务的个体的质量和多样性，可以在所有检查的任务中取得良好的性能。学习到的 Pareto 前沿还表明，具有面向任务的知识共享策略的 GP 算法可以在两个目标上学习 DFJSS 的竞争调度启发式。