Improvements for mlrose applied to the Traveling Salesperson Problem
This work addresses optimization for industrial logistics, but it is incremental as it builds on existing mlrose methods.
The paper tackled the Traveling Salesperson Problem (TSP) as applied to a warehouse storage case, focusing on improving the mlrose library's Genetic Algorithm and Hill Climbing methods by exploiting TSP structure, resulting in shorter tour lengths.
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.