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New classes of efficiently solvable generalized Traveling Salesman Problems

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  • E. Balas

Abstract

We consider the n‐city traveling salesman problem (TSP), symmetric or asymmetric,with the following attributes. In one case, a positive integer k and an ordering (1,..., n) ofthe cities is given, and an optimal tour is sought subject to the condition that for any pairi, j ∈ (1..., n), if j ≥ i + k, then i precedes j in the tour. In another case, position i in the tourhas to be assigned to some city within k positions from i in the above ordering. This case isclosely related to the TSP with time windows. In a third case, an optimal tour visiting m outof n cities is sought subject to constraints of the above two types. This is a special case ofthe Prize Collecting TSP (PCTSP). In any of the three cases, k may be replaced by city‐specificintegers k(i), i=1,..., n. These problems arise in practice. For each class, we reducethe problem to that of finding a shortest source‐sink path in a layered network with a numberof arcs linear in n and exponential in the parameter k (which is independent of the problemsize). Besides providing linear time algorithms for the solution of these problems, the reductionto a shortest path problem also provides a compact linear programming formulation.Finally, for TSPs or PCTSPs that do not have the required attributes, these algorithms canbe used as heuristics that find in linear time a local optimum over an exponential‐sizeneighborhood. Copyright Kluwer Academic Publishers 1999

Suggested Citation

  • E. Balas, 1999. "New classes of efficiently solvable generalized Traveling Salesman Problems," Annals of Operations Research, Springer, vol. 86(0), pages 529-558, January.
  • Handle: RePEc:spr:annopr:v:86:y:1999:i:0:p:529-558:10.1023/a:1018939709890
    DOI: 10.1023/A:1018939709890
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    Cited by:

    1. 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.
    2. Christian Tilk & Stefan Irnich, 2014. "Dynamic Programming for the Minimum Tour Duration Problem," Working Papers 1408, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz, revised 04 Aug 2014.
    3. Eranda Çela & Vladimir G. Deineko & Gerhard J. Woeginger, 2017. "The multi-stripe travelling salesman problem," Annals of Operations Research, Springer, vol. 259(1), pages 21-34, December.
    4. Oleg L. Tashlykov & Alexander N. Sesekin & Alexander G. Chentsov & Alexei A. Chentsov, 2022. "Development of Methods for Route Optimization of Work in Inhomogeneous Radiation Fields to Minimize the Dose Load of Personnel," Energies, MDPI, vol. 15(13), pages 1-11, June.
    5. Nicola Secomandi & François Margot, 2009. "Reoptimization Approaches for the Vehicle-Routing Problem with Stochastic Demands," Operations Research, INFORMS, vol. 57(1), pages 214-230, February.
    6. Vu, Duc Minh & Hewitt, Mike & Vu, Duc D., 2022. "Solving the time dependent minimum tour duration and delivery man problems with dynamic discretization discovery," European Journal of Operational Research, Elsevier, vol. 302(3), pages 831-846.
    7. 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.
    8. 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.
    9. Richard K. Congram & Chris N. Potts & Steef L. van de Velde, 2002. "An Iterated Dynasearch Algorithm for the Single-Machine Total Weighted Tardiness Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 14(1), pages 52-67, February.
    10. 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.
    11. Andre A. Cire & Willem-Jan van Hoeve, 2013. "Multivalued Decision Diagrams for Sequencing Problems," Operations Research, INFORMS, vol. 61(6), pages 1411-1428, December.
    12. Dominique Feillet & Pierre Dejax & Michel Gendreau, 2005. "Traveling Salesman Problems with Profits," Transportation Science, INFORMS, vol. 39(2), pages 188-205, May.
    13. Jayanth Krishna Mogali & Joris Kinable & Stephen F. Smith & Zachary B. Rubinstein, 2021. "Scheduling for multi-robot routing with blocking and enabling constraints," Journal of Scheduling, Springer, vol. 24(3), pages 291-318, June.
    14. 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.
    15. 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.
    16. Christian Tilk & Stefan Irnich, 2017. "Dynamic Programming for the Minimum Tour Duration Problem," Transportation Science, INFORMS, vol. 51(2), pages 549-565, May.

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