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A simulated annealing heuristic for the team orienteering problem with time windows

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  • Lin, Shih-Wei
  • Yu, Vincent F.

Abstract

This paper presents a simulated annealing based heuristic approach for the team orienteering problem with time windows (TOPTW). Given a set of known locations, each with a score, a service time, and a time window, the TOPTW finds a set of vehicle tours that maximizes the total collected scores. Each tour is limited in length and a visit to a location must start within the location’s service time window. The proposed heuristic is applied to benchmark instances. Computational results indicate that the proposed heuristic is competitive with other solution approaches in the literature.

Suggested Citation

  • Lin, Shih-Wei & Yu, Vincent F., 2012. "A simulated annealing heuristic for the team orienteering problem with time windows," European Journal of Operational Research, Elsevier, vol. 217(1), pages 94-107.
    • Handle: RePEc:eee:ejores:v:217:y:2012:i:1:p:94-107
    DOI: 10.1016/j.ejor.2011.08.024
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    References listed on IDEAS

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    Cited by:

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    2. Amine Masmoudi, M. & Mancini, Simona & Baldacci, Roberto & Kuo, Yong-Hong, 2022. "Vehicle routing problems with drones equipped with multi-package payload compartments," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    3. Zhao, Yanlu & Alfandari, Laurent, 2020. "Design of diversified package tours for the digital travel industry : A branch-cut-and-price approach," European Journal of Operational Research, Elsevier, vol. 285(3), pages 825-843.
    4. Mahdi Bashiri & Zeinab Rasoulinejad & Ehsan Fallahzade, 2016. "A new approach on auxiliary vehicle assignment in capacitated location routing problem," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(4), pages 886-902, March.
    5. Vincent F. Yu & Yueh-Sheng Lin & Panca Jodiawan & Shih-Wei Lin & Yu-Chi Lai, 2023. "The Field Technician Scheduling Problem with Experience-Dependent Service Times," Mathematics, MDPI, vol. 11(21), pages 1-17, November.
    6. Rahma Lahyani & Mahdi Khemakhem & Frédéric Semet, 2017. "A unified matheuristic for solving multi-constrained traveling salesman problems with profits," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 5(3), pages 393-422, September.
    7. Shih-Wei Lin & Ching-Jung Ting & Kun-Chih Wu, 2018. "Simulated annealing with different vessel assignment strategies for the continuous berth allocation problem," Flexible Services and Manufacturing Journal, Springer, vol. 30(4), pages 740-763, December.
    8. Margaretha Gansterer & Murat Küçüktepe & Richard F. Hartl, 2017. "The multi-vehicle profitable pickup and delivery problem," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(1), pages 303-319, January.
    9. Damianos Gavalas & Charalampos Konstantopoulos & Konstantinos Mastakas & Grammati Pantziou, 2019. "Efficient Cluster-Based Heuristics for the Team Orienteering Problem with Time Windows," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(01), pages 1-44, February.
    10. Ernesto Tarantino & Ivanoe De Falco & Umberto Scafuri, 2019. "A mobile personalized tourist guide and its user evaluation," Information Technology & Tourism, Springer, vol. 21(3), pages 413-455, September.
    11. Lee, Alan & Savelsbergh, Martin, 2015. "Dynamic ridesharing: Is there a role for dedicated drivers?," Transportation Research Part B: Methodological, Elsevier, vol. 81(P2), pages 483-497.
    12. Mei, Yi & Salim, Flora D. & Li, Xiaodong, 2016. "Efficient meta-heuristics for the Multi-Objective Time-Dependent Orienteering Problem," European Journal of Operational Research, Elsevier, vol. 254(2), pages 443-457.
    13. Thibaut Vidal & Nelson Maculan & Luiz Satoru Ochi & Puca Huachi Vaz Penna, 2016. "Large Neighborhoods with Implicit Customer Selection for Vehicle Routing Problems with Profits," Transportation Science, INFORMS, vol. 50(2), pages 720-734, May.
    14. Antonio R. Uguina & Juan F. Gomez & Javier Panadero & Anna Martínez-Gavara & Angel A. Juan, 2024. "A Learnheuristic Algorithm Based on Thompson Sampling for the Heterogeneous and Dynamic Team Orienteering Problem," Mathematics, MDPI, vol. 12(11), pages 1-19, June.
    15. Gunawan, Aldy & Lau, Hoong Chuin & Vansteenwegen, Pieter, 2016. "Orienteering Problem: A survey of recent variants, solution approaches and applications," European Journal of Operational Research, Elsevier, vol. 255(2), pages 315-332.
    16. Xia, Jun & Wang, Kai & Wang, Shuaian, 2019. "Drone scheduling to monitor vessels in emission control areas," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 174-196.
    17. Hu, Qian & Lim, Andrew, 2014. "An iterative three-component heuristic for the team orienteering problem with time windows," European Journal of Operational Research, Elsevier, vol. 232(2), pages 276-286.
    18. Meng, Lingyun & Zhou, Xuesong, 2019. "An integrated train service plan optimization model with variable demand: A team-based scheduling approach with dual cost information in a layered network," Transportation Research Part B: Methodological, Elsevier, vol. 125(C), pages 1-28.
    19. Zhang, Shu & Ohlmann, Jeffrey W. & Thomas, Barrett W., 2020. "Multi-period orienteering with uncertain adoption likelihood and waiting at customers," European Journal of Operational Research, Elsevier, vol. 282(1), pages 288-303.
    20. Ivanoe De Falco & Umberto Scafuri & Ernesto Tarantino, 2016. "Optimizing Personalized Touristic Itineraries by a Multiobjective Evolutionary Algorithm," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1269-1312, November.

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