IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v268y2018i1p54-69.html
   My bibliography  Save this article

A memetic algorithm for the Orienteering Problem with Mandatory Visits and Exclusionary Constraints

Author

Listed:
  • Lu, Yongliang
  • Benlic, Una
  • Wu, Qinghua

Abstract

The Orienteering Problem with Mandatory Visits and Exclusionary Constraints (OPMVEC) is to visit a set of mandatory nodes (locations) and some optional nodes, while respecting the compatibility constraint between nodes and the maximum total time budget constraint. It is a variation of the classic orienteering problem that originates from a number of real-life applications. We present a highly effective memetic algorithm (MA) for OPMVEC combining: (i) a dedicated tabu search procedure that considers both feasible and infeasible solutions by constraint relaxation, (ii) a backbone-based crossover, and (iii) a randomized mutation procedure to prevent from premature convergence. Experiments on six classes of 340 benchmark instances from the literature demonstrate highly competitive performance of MA – it reports improved results for 104 instances compared with the existing heuristic approach, while finding matching best-known results for the remaining cases. Additionally, MA can be used to produce a starting point for an exact solver (e.g., CPLEX), leading to an increased number of problem instances that are solved to optimality. We further investigate the contribution of the key algorithmic elements to the success of the proposed approach.

Suggested Citation

  • Lu, Yongliang & Benlic, Una & Wu, Qinghua, 2018. "A memetic algorithm for the Orienteering Problem with Mandatory Visits and Exclusionary Constraints," European Journal of Operational Research, Elsevier, vol. 268(1), pages 54-69.
    • Handle: RePEc:eee:ejores:v:268:y:2018:i:1:p:54-69
    DOI: 10.1016/j.ejor.2018.01.019
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221718300377
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2018.01.019?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Michel Gendreau & Alain Hertz & Gilbert Laporte, 1994. "A Tabu Search Heuristic for the Vehicle Routing Problem," Management Science, INFORMS, vol. 40(10), pages 1276-1290, October.
    2. 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.
    3. Chen, Yuning & Hao, Jin-Kao & Glover, Fred, 2016. "A hybrid metaheuristic approach for the capacitated arc routing problem," European Journal of Operational Research, Elsevier, vol. 253(1), pages 25-39.
    4. Rafael Martí & Abraham Duarte & Manuel Laguna, 2009. "Advanced Scatter Search for the Max-Cut Problem," INFORMS Journal on Computing, INFORMS, vol. 21(1), pages 26-38, February.
    5. Matteo Fischetti & Juan José Salazar González & Paolo Toth, 1998. "Solving the Orienteering Problem through Branch-and-Cut," INFORMS Journal on Computing, INFORMS, vol. 10(2), pages 133-148, May.
    6. Pamela J. Palomo-Martínez & M. Angélica Salazar-Aguilar & Víctor M. Albornoz, 2017. "Formulations for the orienteering problem with additional constraints," Annals of Operations Research, Springer, vol. 258(2), pages 503-545, November.
    7. Wu, Qinghua & Hao, Jin-Kao, 2013. "A hybrid metaheuristic method for the Maximum Diversity Problem," European Journal of Operational Research, Elsevier, vol. 231(2), pages 452-464.
    8. Bruce L. Golden & Larry Levy & Rakesh Vohra, 1987. "The orienteering problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 34(3), pages 307-318, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. He, Mu & Wu, Qinghua & Benlic, Una & Lu, Yongliang & Chen, Yuning, 2024. "An effective multi-level memetic search with neighborhood reduction for the clustered team orienteering problem," European Journal of Operational Research, Elsevier, vol. 318(3), pages 778-801.
    2. Shih-Wei Lin & Sirui Guo & Wen-Jie Wu, 2024. "Applying the Simulated Annealing Algorithm to the Set Orienteering Problem with Mandatory Visits," Mathematics, MDPI, vol. 12(19), pages 1-24, October.
    3. Li, Mingjie & Hao, Jin-Kao & Wu, Qinghua, 2024. "A flow based formulation and a reinforcement learning based strategic oscillation for cross-dock door assignment," European Journal of Operational Research, Elsevier, vol. 312(2), pages 473-492.
    4. Yang, Yu & Yan, Chiwei & Cao, Yufeng & Roberti, Roberto, 2023. "Planning robust drone-truck delivery routes under road traffic uncertainty," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1145-1160.
    5. Schulz, Arne & Pfeiffer, Christian, 2024. "A Branch-and-Cut algorithm for the dial-a-ride problem with incompatible customer types," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kobeaga, Gorka & Rojas-Delgado, Jairo & Merino, María & Lozano, Jose A., 2024. "A revisited branch-and-cut algorithm for large-scale orienteering problems," European Journal of Operational Research, Elsevier, vol. 313(1), pages 44-68.
    2. Balcik, Burcu, 2017. "Site selection and vehicle routing for post-disaster rapid needs assessment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 101(C), pages 30-58.
    3. Sadan Kulturel-Konak & Bryan A. Norman & David W. Coit & Alice E. Smith, 2004. "Exploiting Tabu Search Memory in Constrained Problems," INFORMS Journal on Computing, INFORMS, vol. 16(3), pages 241-254, August.
    4. Markus Sinnl, 2021. "Mixed-integer programming approaches for the time-constrained maximal covering routing problem," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(2), pages 497-542, June.
    5. Gambardella, L.M. & Montemanni, R. & Weyland, D., 2012. "Coupling ant colony systems with strong local searches," European Journal of Operational Research, Elsevier, vol. 220(3), pages 831-843.
    6. 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.
    7. Vansteenwegen, Pieter & Souffriau, Wouter & Berghe, Greet Vanden & Oudheusden, Dirk Van, 2009. "A guided local search metaheuristic for the team orienteering problem," European Journal of Operational Research, Elsevier, vol. 196(1), pages 118-127, July.
    8. H Tang & E Miller-Hooks, 2005. "Algorithms for a stochastic selective travelling salesperson problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(4), pages 439-452, April.
    9. Yu, Qinxiao & Fang, Kan & Zhu, Ning & Ma, Shoufeng, 2019. "A matheuristic approach to the orienteering problem with service time dependent profits," European Journal of Operational Research, Elsevier, vol. 273(2), pages 488-503.
    10. Zhou, Qing & Benlic, Una & Wu, Qinghua & Hao, Jin-Kao, 2019. "Heuristic search to the capacitated clustering problem," European Journal of Operational Research, Elsevier, vol. 273(2), pages 464-487.
    11. Bian, Zheyong & Liu, Xiang, 2018. "A real-time adjustment strategy for the operational level stochastic orienteering problem: A simulation-aided optimization approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 115(C), pages 246-266.
    12. Erdogan, Günes & Cordeau, Jean-François & Laporte, Gilbert, 2010. "The Attractive Traveling Salesman Problem," European Journal of Operational Research, Elsevier, vol. 203(1), pages 59-69, May.
    13. Aringhieri, Roberto & Cordone, Roberto & Grosso, Andrea, 2015. "Construction and improvement algorithms for dispersion problems," European Journal of Operational Research, Elsevier, vol. 242(1), pages 21-33.
    14. Dominique Feillet & Pierre Dejax & Michel Gendreau, 2005. "Traveling Salesman Problems with Profits," Transportation Science, INFORMS, vol. 39(2), pages 188-205, May.
    15. Pablo A. Miranda-Gonzalez & Javier Maturana-Ross & Carola A. Blazquez & Guillermo Cabrera-Guerrero, 2021. "Exact Formulation and Analysis for the Bi-Objective Insular Traveling Salesman Problem," Mathematics, MDPI, vol. 9(21), pages 1-33, October.
    16. Krzysztof Ostrowski & Joanna Karbowska-Chilinska & Jolanta Koszelew & Pawel Zabielski, 2017. "Evolution-inspired local improvement algorithm solving orienteering problem," Annals of Operations Research, Springer, vol. 253(1), pages 519-543, June.
    17. Qinxiao Yu & Yossiri Adulyasak & Louis-Martin Rousseau & Ning Zhu & Shoufeng Ma, 2022. "Team Orienteering with Time-Varying Profit," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 262-280, January.
    18. 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.
    19. Bijun Wang & Zheyong Bian & Mo Mansouri, 2023. "Self-adaptive heuristic algorithms for the dynamic and stochastic orienteering problem in autonomous transportation system," Journal of Heuristics, Springer, vol. 29(1), pages 77-137, February.
    20. Verbeeck, C. & Sörensen, K. & Aghezzaf, E.-H. & Vansteenwegen, P., 2014. "A fast solution method for the time-dependent orienteering problem," European Journal of Operational Research, Elsevier, vol. 236(2), pages 419-432.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:268:y:2018:i:1:p:54-69. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.