Scheduling problems are a subclass of combinatorial problems consisting of a set of tasks/activities/jobs to be processed by a set of resources usually to minimize a time criterion. Some optimization methods used to solve these problems are hybridized with knowledge discovery techniques to extract information during the optimization process and enhance it. However, most of these hybrid techniques are custom-designed and lack generalization. In this paper, a module for knowledge extraction in Stochastic Local Searches is designed, aiming to be problem independent and plugged into optimization methods that relies on multiple Stochastic Local Search replications. The objective is to prune parts of the search space for which the exploration is likely to lead to poor solutions. This is performed through the extraction of high-quality patterns occurring in locally optimal solutions. Benchmarked on two well-known scheduling problems, the Job-shop Problem and the Resource Constrained Project Scheduling Problem, the results show both a speed up in the convergence and the reaching of better local optima solutions.

中文翻译：

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调度问题随机局部搜索中基于模式挖掘的剪枝策略

调度问题是组合问题的子类，由一组任务/活动/作业组成，这些任务/活动/作业通常由一组资源处理，以最小化时间标准。一些用于解决这些问题的优化方法与知识发现技术相结合，以在优化过程中提取信息并增强它。然而，这些混合技术中的大多数都是定制设计的，缺乏泛化性。在本文中，设计了一个用于随机局部搜索中的知识提取的模块，旨在独立于问题并插入依赖于多个随机局部搜索复制的优化方法中。目标是修剪搜索空间中的探索可能导致不良解决方案的部分。这是通过提取出现在局部最优解中的高质量模式来执行的。以两个众所周知的调度问题为基准，即作业车间问题和资源受限项目调度问题，结果表明收敛速度加快，并且达到了更好的局部最优解。