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Improved Algorithms for the Capacitated Team Orienteering Problem

Authors Gianlorenzo D'Angelo , Mattia D'Emidio , Esmaeil Delfaraz , Gabriele Di Stefano

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Author Details

Gianlorenzo D'Angelo
  • Gran Sasso Science Institute, L'Aquila, Italy
Mattia D'Emidio
  • Dept. of Information Engineering, Computer Science and Mathematics, University of L'Aquila, L'Aquila, Italy
Esmaeil Delfaraz
  • Dept. of Information Engineering, Computer Science and Mathematics, University of L'Aquila, L'Aquila, Italy
Gabriele Di Stefano
  • Dept. of Information Engineering, Computer Science and Mathematics, University of L'Aquila, L'Aquila, Italy

Funding

This work was partially supported by: Project EMERGE: innovation agreement between Ministry of Economical Development, Abruzzo Region, Radiolabs, Elital, Leonardo, Telespazio, University of L’Aquila and Centre of EXcellence EX-Emerge, funded by Italian Government under CIPE n. 70/2017; Group for Scientific Computation of Istituto Nazionale di Alta Matematica (GNCS-INdAM); the European Union - NextGenerationEU under the Italian Ministry of University and Research National Innovation Ecosystem grant ECS00000041 - VITALITY - CUP: D13C21000430001; PNRR MUR project GAMING "Graph Algorithms and MinINg for Green agents" (PE0000013, CUP D13C24000430001); the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on "Telecommunications of the Future" (PE00000001 - program RESTART), project MoVeOver/SCHEDULE ("Smart interseCtions witH connEcteD and aUtonomous vehicLEs", CUP J33C22002880001); ARS01_00540 - RASTA project, funded by the Italian Ministry of Research PNR 2015-2020. The first author acknowledges the support of the MUR (Italy) Department of Excellence 2023-2027.

Cite As Get BibTex

Gianlorenzo D'Angelo, Mattia D'Emidio, Esmaeil Delfaraz, and Gabriele Di Stefano. Improved Algorithms for the Capacitated Team Orienteering Problem. In 24th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2024). Open Access Series in Informatics (OASIcs), Volume 123, pp. 7:1-7:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/OASIcs.ATMOS.2024.7

Abstract

We study the Capacitated Team Orienteering Problem, where a fleet of vehicles with capacities have to meet customers with known demands and prizes for a single commodity.
The objective is to maximize the total prize and to assign a sequence of customers to each vehicle while keeping the total distance traveled within a given budget and such that the total demand served by each vehicle does not exceed its capacity.
The problem has been widely studied both from a theoretical and a practical point of view. The contribution of this paper is twofold: (1) We advance the theoretical knowledge on the problem by providing new approximation algorithms that achieve, under some natural assumption, improved approximation ratios compared to the current best algorithms; (2) We propose four efficient heuristics that outperform the current state-of-the-art practical methods in the sense that they compute solutions that collect nearly the same prize in a significantly smaller running time. We also experimentally test the scalability of the new heuristics, showing that their running time increases approximately linearly with the size of the input, allowing us to process large graphs which were not possible to analyze before.

Subject Classification

ACM Subject Classification
  • Theory of computation → Graph algorithms analysis
  • Theory of computation → Approximation algorithms analysis
  • Theory of computation → Design and analysis of algorithms
Keywords
  • Vehicle Routing
  • Approximation algorithms
  • Algorithm Engineering

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