Designing effective dispatching rules for production systems is a difficult and time-consuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This article develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes.

中文翻译：

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一种用于调度规则自动化设计的混合遗传编程算法

如果手动完成，为生产系统设计有效的调度规则是一项困难且耗时的任务。在过去十年中，计算能力的增长、先进的机器学习和优化技术使调度规则的自动化设计成为可能，并且自动发现的规则具有竞争力或优于研究人员开发的现有规则。遗传编程是文献中发现调度规则最流行的方法之一，尤其是对于复杂的生产系统。然而，大型启发式搜索空间可能会限制遗传编程找到接近最优的调度规则。本文基于新的表示、新的局部搜索启发式和高效的适应度评估器，为动态作业车间调度开发了一种新的混合遗传编程算法。实验表明，新方法对进化规则的质量是有效的。此外，进化规则也明显更小，包含更多相关属性。