Improvements for mlrose Applied to the Traveling Salesperson Problem.

Stefan Wintersteller; Martin Uray; Michael Lehenauer; Stefan Huber

doi:10.1007/978-3-031-25312-6_72

Improvements for mlrose Applied to the Traveling Salesperson Problem.

Stefan Wintersteller, Martin Uray, Michael Lehenauer, Stefan Huber

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	EUROCAST
Pages	611-618
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-031-25312-6_72
Publication status	Published - 10 Feb 2023

Classification according to Österreichische Systematik der Wissenschaftszweige (ÖFOS 2012)

102001 Artificial intelligence

Applied Research Level (ARL)

ARL Level 3 - Proof of the functionality of a principle

Research focus/foci

Industrial Informatics

Access to Document

10.1007/978-3-031-25312-6_72

Cite this

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title = "Improvements for mlrose Applied to the Traveling Salesperson Problem.",

abstract = "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.",

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AU - Lehenauer, Michael

AU - Huber, Stefan

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PY - 2023/2/10

Y1 - 2023/2/10

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-031-25312-6_72

DO - 10.1007/978-3-031-25312-6_72

M3 - Conference contribution

SP - 611

EP - 618

BT - EUROCAST

ER -

Improvements for mlrose Applied to the Traveling Salesperson Problem.

Abstract

Classification according to Österreichische Systematik der Wissenschaftszweige (ÖFOS 2012)

Applied Research Level (ARL)

Research focus/foci

Access to Document

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Cite this