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Bicriterion Shortest Paths in Stochastic Time-Dependent Networks

In: Multiobjective Programming and Goal Programming

Author

Listed:
  • Lars Relund Nielsen

    (Research Unit of Statistics and Decision Analysis, University of Aarhus)

  • Daniele Pretolani

    (University of Modena and Reggio Emilia)

  • Kim Allan Andersen

    (University of Aarhus)

Abstract

In recent years there has been a growing interest in using stochastic time-dependent (STD) networks as a modelling tool for a number of applications within such areas as transportation and telecommunications. It is known that an optimal routing policy does not necessarily correspond to a path, but rather to a time-adaptive strategy. In some applications, however, it makes good sense to require that the routing policy should correspond to a loopless path in the network, that is, the time-adaptive aspect disappears and a priori route choice is considered. In this paper we consider bicriterion a priori route choice in STD networks, i.e. the problem of finding the set of efficient paths. Both expectation and min—max criteria are considered and a solution method based on the two-phase method is devised. Experimental results reveal that the full set of efficient solutions can be determined on rather large test instances, which is in contrast to the time-adaptive case.

Suggested Citation

  • Lars Relund Nielsen & Daniele Pretolani & Kim Allan Andersen, 2009. "Bicriterion Shortest Paths in Stochastic Time-Dependent Networks," Lecture Notes in Economics and Mathematical Systems, in: Vincent Barichard & Matthias Ehrgott & Xavier Gandibleux & Vincent T'Kindt (ed.), Multiobjective Programming and Goal Programming, pages 57-67, Springer.
    • Handle: RePEc:spr:lnechp:978-3-540-85646-7_6
    DOI: 10.1007/978-3-540-85646-7_6
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    Citations

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    Cited by:

    1. Nielsen, Lars Relund & Andersen, Kim Allan & Pretolani, Daniele, 2014. "Ranking paths in stochastic time-dependent networks," European Journal of Operational Research, Elsevier, vol. 236(3), pages 903-914.
    2. Lars Relund Nielsen & Erik Jørgensen & Søren Højsgaard, 2011. "Embedding a state space model into a Markov decision process," Annals of Operations Research, Springer, vol. 190(1), pages 289-309, October.

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