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The 2-period balanced traveling salesman problem


  • Tatiana Bassetto

    () (Department of Applied Mathematics, University of Venice)

  • Francesco Mason

    () (Department of Applied Mathematics, University of Venice)


In the 2-period Balanced Traveling Salesman Problem (2B-TSP), the customers must be visited over a period of two days: some must be visited daily, and the others on alternate days (even or odd days); moreover, the number of customers visited in every tour must be balancedâ, i.e. it must be the same or, alternatively, the difference between the maximum and the minimum number of visited customers must be less than a given threshold. The salesman's objective is to minimize the total distance travelled over the two tours. Although this problem may be viewed as a particular case of the Period Traveling Salesman Problem, in the 2-period Balanced TSP the assumptions allow for emphasizing on routing aspects, more than on the assignment of the customers to the various days of the period. The paper proposes two heuristic algorithms particularly suited for the case of Euclidean distances between the customers. Computational experiences and a comparison between the two algorithms are also given.

Suggested Citation

  • Tatiana Bassetto & Francesco Mason, 2007. "The 2-period balanced traveling salesman problem," Working Papers 154, Department of Applied Mathematics, Università Ca' Foscari Venezia, revised Oct 2007.
  • Handle: RePEc:vnm:wpaper:154

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    References listed on IDEAS

    1. Baptista, Susana & Oliveira, Rui Carvalho & Zuquete, Eduardo, 2002. "A period vehicle routing case study," European Journal of Operational Research, Elsevier, vol. 139(2), pages 220-229, June.
    2. Laporte, Gilbert, 1992. "The traveling salesman problem: An overview of exact and approximate algorithms," European Journal of Operational Research, Elsevier, vol. 59(2), pages 231-247, June.
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    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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