IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v309y2023i2p488-505.html
   My bibliography  Save this article

Precedence constrained generalized traveling salesman problem: Polyhedral study, formulations, and branch-and-cut algorithm

Author

Listed:
  • Khachai, Daniil
  • Sadykov, Ruslan
  • Battaia, Olga
  • Khachay, Michael

Abstract

The Precedence Constrained Generalized Traveling Salesman Problem (PCGTSP) is an extension of two well-known combinatorial optimization problems — the Generalized Traveling Salesman Problem (GTSP) and the Precedence Constrained Asymmetric Traveling Salesman Problem (PCATSP), whose path version is known as the Sequential Ordering Problem (SOP). Similarly to the classic GTSP, the goal of the PCGTSP, for a given input digraph and partition of its node set into clusters, is to find a minimum cost cyclic route (tour) visiting each cluster in a single node. In addition, as in the PCATSP, feasible tours are restricted to visit the clusters with respect to the given partial order. Unlike the GTSP and SOP, to the best of our knowledge, the PCGTSP still remain to be weakly studied both in terms of polyhedral theory and algorithms. In this paper, for the first time for the PCGTSP, we propose several families of valid inequalities, establish dimension of the PCGTS polytope and prove sufficient conditions ensuring that the extended Balas’ π- and σ-inequalities become facet-inducing. Relying on these theoretical results and evolving the state-of-the-art algorithmic approaches for the PCATSP and SOP, we introduce a family of MILP-models (formulations) and several variants of the branch-and-cut algorithm for the PCGTSP. We prove their high performance in a competitive numerical evaluation against the public benchmark library PCGTSPLIB, a known adaptation of the classic SOPLIB to the problem in question.

Suggested Citation

  • Khachai, Daniil & Sadykov, Ruslan & Battaia, Olga & Khachay, Michael, 2023. "Precedence constrained generalized traveling salesman problem: Polyhedral study, formulations, and branch-and-cut algorithm," European Journal of Operational Research, Elsevier, vol. 309(2), pages 488-505.
  • Handle: RePEc:eee:ejores:v:309:y:2023:i:2:p:488-505
    DOI: 10.1016/j.ejor.2023.01.039
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221723000735
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2023.01.039?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. Thomas L. Morin & Roy E. Marsten, 1976. "Branch-and-Bound Strategies for Dynamic Programming," Operations Research, INFORMS, vol. 24(4), pages 611-627, August.
    2. Hanif D. Sherali & Patrick J. Driscoll, 2002. "On Tightening the Relaxations of Miller-Tucker-Zemlin Formulations for Asymmetric Traveling Salesman Problems," Operations Research, INFORMS, vol. 50(4), pages 656-669, August.
    3. T.A. Makarovskikh & A.V. Panyukov & E.A. Savitskiy, 2018. "Mathematical models and routing algorithms for economical cutting tool paths," International Journal of Production Research, Taylor & Francis Journals, vol. 56(3), pages 1171-1188, February.
    4. Karapetyan, D. & Gutin, G., 2012. "Efficient local search algorithms for known and new neighborhoods for the generalized traveling salesman problem," European Journal of Operational Research, Elsevier, vol. 219(2), pages 234-251.
    5. Andre A. Cire & Willem-Jan van Hoeve, 2013. "Multivalued Decision Diagrams for Sequencing Problems," Operations Research, INFORMS, vol. 61(6), pages 1411-1428, December.
    6. G. Dantzig & R. Fulkerson & S. Johnson, 1954. "Solution of a Large-Scale Traveling-Salesman Problem," Operations Research, INFORMS, vol. 2(4), pages 393-410, November.
    7. Gouveia, Luis & Pires, Jose Manuel, 1999. "The asymmetric travelling salesman problem and a reformulation of the Miller-Tucker-Zemlin constraints," European Journal of Operational Research, Elsevier, vol. 112(1), pages 134-146, January.
    8. Yuan, Yuan & Cattaruzza, Diego & Ogier, Maxime & Semet, Frédéric, 2020. "A branch-and-cut algorithm for the generalized traveling salesman problem with time windows," European Journal of Operational Research, Elsevier, vol. 286(3), pages 849-866.
    9. Matteo Fischetti & Juan José Salazar González & Paolo Toth, 1997. "A Branch-and-Cut Algorithm for the Symmetric Generalized Traveling Salesman Problem," Operations Research, INFORMS, vol. 45(3), pages 378-394, June.
    10. Alexander G. Chentsov & Pavel A. Chentsov & Alexander A. Petunin & Alexander N. Sesekin, 2018. "Model of megalopolises in the tool path optimisation for CNC plate cutting machines," International Journal of Production Research, Taylor & Francis Journals, vol. 56(14), pages 4819-4830, July.
    11. Escudero, L. F., 1988. "An inexact algorithm for the sequential ordering problem," European Journal of Operational Research, Elsevier, vol. 37(2), pages 236-249, November.
    12. Egon Balas & Neil Simonetti, 2001. "Linear Time Dynamic-Programming Algorithms for New Classes of Restricted TSPs: A Computational Study," INFORMS Journal on Computing, INFORMS, vol. 13(1), pages 56-75, February.
    Full references (including those not matched with items on IDEAS)

    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. Rostami, Borzou & Malucelli, Federico & Belotti, Pietro & Gualandi, Stefano, 2016. "Lower bounding procedure for the asymmetric quadratic traveling salesman problem," European Journal of Operational Research, Elsevier, vol. 253(3), pages 584-592.
    2. Balma, Ali & Salem, Safa Ben & Mrad, Mehdi & Ladhari, Talel, 2018. "Strong multi-commodity flow formulations for the asymmetric traveling salesman problem," European Journal of Operational Research, Elsevier, vol. 271(1), pages 72-79.
    3. Gharehgozli, Amir & Zaerpour, Nima, 2020. "Robot scheduling for pod retrieval in a robotic mobile fulfillment system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    4. Zang, Xiaoning & Jiang, Li & Liang, Changyong & Fang, Xiang, 2023. "Coordinated home and locker deliveries: An exact approach for the urban delivery problem with conflicting time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    5. Yuan, Yuan & Cattaruzza, Diego & Ogier, Maxime & Semet, Frédéric, 2020. "A branch-and-cut algorithm for the generalized traveling salesman problem with time windows," European Journal of Operational Research, Elsevier, vol. 286(3), pages 849-866.
    6. Jeanette Schmidt & Stefan Irnich, 2020. "New Neighborhoods and an Iterated Local Search Algorithm for the Generalized Traveling Salesman Problem," Working Papers 2020, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    7. Salii, Yaroslav, 2019. "Revisiting dynamic programming for precedence-constrained traveling salesman problem and its time-dependent generalization," European Journal of Operational Research, Elsevier, vol. 272(1), pages 32-42.
    8. Og[breve]uz, Ceyda & Sibel Salman, F. & Bilgintürk YalçIn, Zehra, 2010. "Order acceptance and scheduling decisions in make-to-order systems," International Journal of Production Economics, Elsevier, vol. 125(1), pages 200-211, May.
    9. Timo Hintsch, 2019. "Large Multiple Neighborhood Search for the Soft-Clustered Vehicle-Routing Problem," Working Papers 1904, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    10. Bakker, Steffen J. & Wang, Akang & Gounaris, Chrysanthos E., 2021. "Vehicle routing with endogenous learning: Application to offshore plug and abandonment campaign planning," European Journal of Operational Research, Elsevier, vol. 289(1), pages 93-106.
    11. Mehdi El Krari & Belaïd Ahiod & Youssef Bouazza El Benani, 2021. "A pre-processing reduction method for the generalized travelling salesman problem," Operational Research, Springer, vol. 21(4), pages 2543-2591, December.
    12. Asef-Vaziri, Ardavan & Kazemi, Morteza, 2018. "Covering and connectivity constraints in loop-based formulation of material flow network design in facility layout," European Journal of Operational Research, Elsevier, vol. 264(3), pages 1033-1044.
    13. Hintsch, Timo & Irnich, Stefan, 2018. "Large multiple neighborhood search for the clustered vehicle-routing problem," European Journal of Operational Research, Elsevier, vol. 270(1), pages 118-131.
    14. Gharehgozli, Amir & Yu, Yugang & de Koster, René & Du, Shaofu, 2019. "Sequencing storage and retrieval requests in a container block with multiple open locations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 261-284.
    15. Duygu Pamukcu & Burcu Balcik, 2020. "A multi-cover routing problem for planning rapid needs assessment under different information-sharing settings," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(1), pages 1-42, March.
    16. Letchford, Adam N. & Nasiri, Saeideh D. & Theis, Dirk Oliver, 2013. "Compact formulations of the Steiner Traveling Salesman Problem and related problems," European Journal of Operational Research, Elsevier, vol. 228(1), pages 83-92.
    17. de Weerdt, Mathijs & Baart, Robert & He, Lei, 2021. "Single-machine scheduling with release times, deadlines, setup times, and rejection," European Journal of Operational Research, Elsevier, vol. 291(2), pages 629-639.
    18. Gianpaolo Ghiani & Gilbert Laporte & Frédéric Semet, 2006. "The Black and White Traveling Salesman Problem," Operations Research, INFORMS, vol. 54(2), pages 366-378, April.
    19. Timo Hintsch & Stefan Irnich, 2017. "Large Multiple Neighborhood Search for the Clustered Vehicle-Routing Problem," Working Papers 1701, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    20. Bernardino, Raquel & Paias, Ana, 2018. "Solving the family traveling salesman problem," European Journal of Operational Research, Elsevier, vol. 267(2), pages 453-466.

    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:309:y:2023:i:2:p:488-505. 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.