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  1. Home > Articles & Issues >
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  3. Makespan Trade-offs ...
Article

Makespan Trade-offs for Visiting Triangle Edges

Konstantinos Georgiou, Somnath Kundu, Pawel Pralat ORCID
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Open on arXiv
Submitted on
November 19, 2021
Accepted on
December 10, 2024
Published on
February 16, 2025
Last modified on
March 27, 2025
Volume 26
vol. 26:3
Discrete Algorithms
DOI
10.46298/dmtcs.8729
License
Attribution 4.0 International (CC BY 4.0)
Natural Sciences and Engineering Research Council of Canada
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633
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765
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Makespan Trade-offs for Visiting Triangle Edges

Konstantinos Georgiou, Somnath Kundu, Pawel Pralat ORCID
Abstract
We study a primitive vehicle routing-type problem in which a fleet of $n$unit speed robots start from a point within a non-obtuse triangle $\Delta$, where $n \in \{1,2,3\}$. The goal is to design robots' trajectories so as to visit all edges of the triangle with the smallest visitation time makespan. We begin our study by introducing a framework for subdividing $\Delta$into regions with respect to the type of optimal trajectory that each point $P$ admits, pertaining to the order that edges are visited and to how the cost of the minimum makespan $R_n(P)$ is determined, for $n\in \{1,2,3\}$. These subdivisions are the starting points for our main result, which is to study makespan trade-offs with respect to the size of the fleet. In particular, we define $ R_{n,m} (\Delta)= \max_{P \in \Delta} R_n(P)/R_m(P)$, and we prove that, over all non-obtuse triangles $\Delta$: (i) $R_{1,3}(\Delta)$ ranges from $\sqrt{10}$ to $4$, (ii) $R_{2,3}(\Delta)$ ranges from $\sqrt{2}$ to $2$, and (iii) $R_{1,2}(\Delta)$ ranges from $5/2$ to $3$. In every case, we pinpoint the starting points within every triangle $\Delta$ that maximize $R_{n,m} (\Delta)$, as well as we identify the triangles that determine all $\inf_\Delta R_{n,m}(\Delta)$ and $\sup_\Delta R_{n,m}(\Delta)$ over the set of non-obtuse triangles. Comment: 47 pages, 27 figures
Keywords
  • Computer Science - Discrete Mathematics
Mathematics Subject Classification 2020
  • 68T40 - Artificial intelligence for robotics
  • 68U05 - Computer graphics; computational geometry (digital and algorithmic aspects)
  • 90B06 - Transportation, logistics and supply chain management
  • 90C27 - Combinatorial optimization
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