This paper studies the computation of ensembles of priority rules for the One Machine Scheduling Problem with variable capacity and total tardiness minimization. Concretely, we address the problem of building optimal ensembles of priority rules, starting from a pool of rules evolved by a Genetic Programming approach. Building on earlier work, we propose a number of new algorithms. These include an iterated greedy search method, a local search algorithm and a memetic algorithm. Experimental results show the potential of the proposed approaches.

中文翻译：

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通过混合进化算法学习在线调度的优先级规则集合

本文研究了可变容量和总时延最小化的单机调度问题优先级规则集合的计算。具体而言，我们从遗传编程方法演变而来的规则库开始，解决构建优先级规则的最佳集合的问题。在早期工作的基础上，我们提出了许多新算法。这些包括迭代贪婪搜索方法，局部搜索算法和模因算法。实验结果表明了该方法的潜力。