In this paper we discuss the application of Artificial Intelligence (AI) to the exemplary industrial use case of the two-dimensional commissioning problem in a high-bay storage, which essentially can be phrased as an instance of Traveling Salesperson Problem (TSP). We investigate the mlrose library that provides an TSP optimizer based on various heuristic optimization techniques. Our focus is on two methods, namely Genetic Algorithm (GA) and Hill Climbing (HC), which are provided by mlrose. We present improvements for both methods that yield shorter tour lengths, by moderately exploiting the problem structure of TSP. That is, the proposed improvements have a generic character and are not limited to TSP only.
翻译:在本文中,我们讨论了将人工智能(AI)应用于高架库存中的二维委托问题的举例工业用例,这本质上可以形成旅行推销员问题(TSP)的实例。我们调查了mlrose库,该库基于各种启发式优化技术提供了TSP优化器。我们的重点是两种方法,即遗传算法(GA)和爬山算法(HC),它们都由mlrose提供。我们介绍了两种方法的改进,这些改进可以通过适度利用TSP的问题结构来缩短路线长度。也就是说,所提出的改进具有通用性,不仅限于TSP问题。