Statistical Analysis of the Performance of the State-of-the-Art Methods for Solving TSP Variants

Boldizsár Tüű-Szabó, Péter Földesi, László T. Kóczy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we analyze the efficiency of the state-of-the-art methods for solving two TSP variants, the Traveling Salesman Problem with Time Windows and one-commodity Pickup-and-Delivery Traveling Salesman Problem. Three models (polynomial, exponential, square-root exponential) were fitted to the mean run times of each method. The parameters of the curves, the R2-values and the RMSE values were compared.

Original languageEnglish
Title of host publicationMulti-disciplinary Trends in Artificial Intelligence - 13th International Conference, MIWAI 2019, Proceedings
EditorsRapeeporn Chamchong, Kok Wai Wong
PublisherSpringer
Pages255-262
Number of pages8
ISBN (Print)9783030337087
DOIs
Publication statusPublished - Jan 1 2019
Event13th Multi-disciplinary International Conference on Artificial Intelligence, MIWAI 2019 - Kuala Lumpur, Malaysia
Duration: Nov 17 2019Nov 19 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11909 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th Multi-disciplinary International Conference on Artificial Intelligence, MIWAI 2019
CountryMalaysia
CityKuala Lumpur
Period11/17/1911/19/19

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Keywords

  • Delivery
  • Pick up
  • Time windows
  • Traveling Salesman Problem

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Tüű-Szabó, B., Földesi, P., & Kóczy, L. T. (2019). Statistical Analysis of the Performance of the State-of-the-Art Methods for Solving TSP Variants. In R. Chamchong, & K. W. Wong (Eds.), Multi-disciplinary Trends in Artificial Intelligence - 13th International Conference, MIWAI 2019, Proceedings (pp. 255-262). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11909 LNAI). Springer. https://doi.org/10.1007/978-3-030-33709-4_23