Abstract The job Sequencing and tool Switching Problem (SSP) has been extensively studied in the field of operations research, due to its practical relevance and methodological interest. Given a machine that can load a limited amount of tools simultaneously and a number of jobs that require a subset of the available tools, the SSP seeks a job sequence that minimizes the number of tool switches in the machine. To solve this problem, we propose a simple and efficient hybrid genetic search based on a generic solution representation, a tailored decoding operator, efficient local searches and diversity management techniques. To guide the search, we introduce a secondary objective designed to break ties. These techniques allow to explore structurally different solutions and escape local optima. As shown in our computational experiments on classical benchmark instances, our algorithm significantly outperforms all previous approaches while remaining simple to apprehend and easy to implement. We finally report results on a new set of larger instances to stimulate future research and comparative analyses.

中文翻译：

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工作排序和工具切换问题的简单有效的混合遗传搜索

摘要 作业排序和工具切换问题（SSP）由于其实际相关性和方法论意义，在运筹学领域得到了广泛研究。给定一台可以同时装载有限数量刀具的机器和需要可用刀具子集的多个作业，SSP 寻求一个作业序列，以最大限度地减少机器中刀具切换的数量。为了解决这个问题，我们提出了一种基于通用解决方案表示、定制解码算子、高效局部搜索和多样性管理技术的简单有效的混合遗传搜索。为了指导搜索，我们引入了一个旨在打破联系的次要目标。这些技术允许探索结构上不同的解决方案并避开局部最优。正如我们在经典基准实例上的计算实验所示，我们的算法显着优于所有以前的方法，同时保持易于理解和易于实现。我们最终报告了一组新的更大实例的结果，以刺激未来的研究和比较分析。