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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 AsGet 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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References

  1. Claudia Archetti, Nicola Bianchessi, and Maria Grazia Speranza. Optimal solutions for routing problems with profits. Discret. Appl. Math., 161(4-5):547-557, 2013. Google Scholar
  2. Claudia Archetti, Dominique Feillet, Alain Hertz, and Maria Grazia Speranza. The capacitated team orienteering and profitable tour problems. J. Oper. Res. Soc., 60(6):831-842, 2009. Google Scholar
  3. Florian Arnold, Michel Gendreau, and Kenneth Sörensen. Efficiently solving very large-scale routing problems. Comput. Oper. Res., 107:32-42, 2019. Google Scholar
  4. Sankalp Arora and Sebastian A. Scherer. Randomized algorithm for informative path planning with budget constraints. In 2017 IEEE International Conference on Robotics and Automation, ICRA 2017, Singapore, Singapore, May 29 - June 3, 2017, pages 4997-5004. IEEE, 2017. Google Scholar
  5. Nikhil Bansal, Avrim Blum, Shuchi Chawla, and Adam Meyerson. Approximation algorithms for deadline-tsp and vehicle routing with time-windows. In László Babai, editor, Proceedings of the 36th Annual ACM Symposium on Theory of Computing, Chicago, IL, USA, June 13-16, 2004, pages 166-174. ACM, 2004. Google Scholar
  6. Asma Ben-Said, Racha El-Hajj, and Aziz Moukrim. A variable space search heuristic for the capacitated team orienteering problem. J. Heuristics, 25(2):273-303, 2019. Google Scholar
  7. Avrim Blum, Shuchi Chawla, David R. Karger, Terran Lane, Adam Meyerson, and Maria Minkoff. Approximation algorithms for orienteering and discounted-reward TSP. SIAM J. Comput., 37(2):653-670, 2007. Google Scholar
  8. Adrian Bock and Laura Sanità. The capacitated orienteering problem. Discret. Appl. Math., 195:31-42, 2015. Google Scholar
  9. Chandra Chekuri, Nitish Korula, and Martin Pál. Improved algorithms for orienteering and related problems. ACM Trans. Algorithms, 8(3):23:1-23:27, 2012. Google Scholar
  10. Ke Chen and Sariel Har-Peled. The euclidean orienteering problem revisited. SIAM J. Comput., 38(1):385-397, 2008. Google Scholar
  11. Nicos Christofides. The vehicle routing problem. Revue française d'automatique, informatique, recherche opérationnelle. Recherche opérationnelle, 10(V1):55-70, 1976. Google Scholar
  12. Gianlorenzo D'Angelo, Esmaeil Delfaraz, and Hugo Gilbert. Budgeted out-tree maximization with submodular prizes. In Sang Won Bae and Heejin Park, editors, 33rd International Symposium on Algorithms and Computation, ISAAC 2022, December 19-21, 2022, Seoul, Korea, volume 248 of LIPIcs, pages 9:1-9:19. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022. Google Scholar
  13. Mattia D’Emidio, Esmaeil Delfaraz, Gabriele Di Stefano, Giannantonio Frittella, and Edgardo Vittoria. Route planning algorithms for fleets of connected vehicles: State of the art, implementation, and deployment. Applied Sciences, 14(7), 2024. URL: https://doi.org/10.3390/app14072884.
  14. Zachary Friggstad, Sreenivas Gollapudi, Kostas Kollias, Tamás Sarlós, Chaitanya Swamy, and Andrew Tomkins. Orienteering algorithms for generating travel itineraries. In Yi Chang, Chengxiang Zhai, Yan Liu, and Yoelle Maarek, editors, Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM 2018, Marina Del Rey, CA, USA, February 5-9, 2018, pages 180-188. ACM, 2018. Google Scholar
  15. Zachary Friggstad and Chaitanya Swamy. Compact, provably-good lps for orienteering and regret-bounded vehicle routing. In Friedrich Eisenbrand and Jochen Könemann, editors, Integer Programming and Combinatorial Optimization - 19th International Conference, IPCO 2017, Waterloo, ON, Canada, June 26-28, 2017, Proceedings, volume 10328 of Lecture Notes in Computer Science, pages 199-211. Springer, 2017. Google Scholar
  16. Aldy Gunawan, Kien Ming Ng, Vincent F Yu, Gordy Adiprasetyo, and Hoong Chuin Lau. The capacitated team orienteering problem. Proceedings of the 9th International Conference on Industrial Engineering and Operations ManagementBangkok, Thailand, March 5-7, 2019, pages 1630-1638, 2019. Google Scholar
  17. Aldy Gunawan, Jiahui Zhu, and Kien Ming NG. The capacitated team orienteering problem: a hybrid simulated annealing and iterated local search approach. In Proceedings of the 13th International Conference on the Practice and Theory of Automated Timetabling-PATAT, volume 2, 2021. Google Scholar
  18. Anupam Gupta, Ravishankar Krishnaswamy, Viswanath Nagarajan, and R. Ravi. Running errands in time: Approximation algorithms for stochastic orienteering. Math. Oper. Res., 40(1):56-79, 2015. Google Scholar
  19. Farouk Hammami. An efficient hybrid adaptive large neighborhood search method for the capacitated team orienteering problem. Expert Systems with Applications, 249:123561, 2024. Google Scholar
  20. André Hottung and Kevin Tierney. Neural large neighborhood search for routing problems. Artif. Intell., 313:103786, 2022. Google Scholar
  21. Tung-Wei Kuo, Kate Ching-Ju Lin, and Ming-Jer Tsai. Maximizing submodular set function with connectivity constraint: Theory and application to networks. IEEE/ACM Trans. Netw., 23(2):533-546, 2015. Google Scholar
  22. Zhixing Luo, Brenda Cheang, Andrew Lim, and Wenbin Zhu. An adaptive ejection pool with toggle-rule diversification approach for the capacitated team orienteering problem. Eur. J. Oper. Res., 229(3):673-682, 2013. Google Scholar
  23. Alice Paul, Daniel Freund, Aaron M. Ferber, David B. Shmoys, and David P. Williamson. Budgeted prize-collecting traveling salesman and minimum spanning tree problems. Math. Oper. Res., 45(2):576-590, 2020. Google Scholar
  24. Sandro Pirkwieser and Günther R. Raidl. Multilevel variable neighborhood search for periodic routing problems. In Peter I. Cowling and Peter Merz, editors, Evolutionary Computation in Combinatorial Optimization, 10th European Conference, EvoCOP 2010, Istanbul, Turkey, April 7-9, 2010. Proceedings, volume 6022 of Lecture Notes in Computer Science, pages 226-238. Springer, 2010. Google Scholar
  25. Christos D. Tarantilis, Foteini Stavropoulou, and Panagiotis P. Repoussis. The capacitated team orienteering problem: A bi-level filter-and-fan method. Eur. J. Oper. Res., 224(1):65-78, 2013. Google Scholar
  26. Dimitra Trachanatzi, Manousos Rigakis, Andromachi Taxidou, Magdalene Marinaki, Yannis Marinakis, and Nikolaos F. Matsatsinis. A novel solution encoding in the differential evolution algorithm for optimizing tourist trip design problems. In Nikolaos F. Matsatsinis, Yannis Marinakis, and Panos M. Pardalos, editors, Learning and Intelligent Optimization - 13th International Conference, LION 13, Chania, Crete, Greece, May 27-31, 2019, Revised Selected Papers, volume 11968 of Lecture Notes in Computer Science, pages 253-267. Springer, 2019. Google Scholar
  27. Vijay V Vazirani. Approximation algorithms, volume 1. Springer, 2001. Google Scholar
  28. Wenzheng Xu, Weifa Liang, Zichuan Xu, Jian Peng, Dezhong Peng, Tang Liu, Xiaohua Jia, and Sajal K. Das. Approximation algorithms for the generalized team orienteering problem and its applications. IEEE/ACM Trans. Netw., 29(1):176-189, 2021. Google Scholar
  29. Wenzheng Xu, Chengxi Wang, Hongbin Xie, Weifa Liang, Haipeng Dai, Zichuan Xu, Ziming Wang, Bing Guo, and Sajal K Das. Reward maximization for disaster zone monitoring with heterogeneous uavs. IEEE/ACM Transactions on Networking, 2023. Google Scholar
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