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A comparison of heuristic best-first algorithms for bicriterion shortest path problems

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Listed:
  • Machuca, E.
  • Mandow, L.
  • Pérez de la Cruz, J.L.
  • Ruiz-Sepulveda, A.

Abstract

A variety of algorithms have been proposed to solve the bicriterion shortest path problem. This article analyzes and compares the performance of three best-first (label-setting) algorithms that accept heuristic information to improve efficiency. These are NAMOA∗, MOA∗, and Tung & Chew’s algorithm (TC). A set of experiments explores the impact of heuristic information in search efficiency, and the relative performance of the algorithms. The analysis reveals that NAMOA∗ is the best option for difficult problems. Its time performance can benefit considerably from heuristic information, though not in all cases. The performance of TC is similar but somewhat worse. However, the time performance of MOA∗ is found to degrade considerably with the use of heuristic information in most cases. Explanations are provided for these phenomena.

Suggested Citation

  • Machuca, E. & Mandow, L. & Pérez de la Cruz, J.L. & Ruiz-Sepulveda, A., 2012. "A comparison of heuristic best-first algorithms for bicriterion shortest path problems," European Journal of Operational Research, Elsevier, vol. 217(1), pages 44-53.
    • Handle: RePEc:eee:ejores:v:217:y:2012:i:1:p:44-53
    DOI: 10.1016/j.ejor.2011.08.030
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    References listed on IDEAS

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    1. Ziliaskopoulos, Athanasios K. & Mandanas, Fotios D. & Mahmassani, Hani S., 2009. "An extension of labeling techniques for finding shortest path trees," European Journal of Operational Research, Elsevier, vol. 198(1), pages 63-72, October.
    2. Matthias Müller-Hannemann & Karsten Weihe, 2006. "On the cardinality of the Pareto set in bicriteria shortest path problems," Annals of Operations Research, Springer, vol. 147(1), pages 269-286, October.
    3. Mote, John & Murthy, Ishwar & Olson, David L., 1991. "A parametric approach to solving bicriterion shortest path problems," European Journal of Operational Research, Elsevier, vol. 53(1), pages 81-92, July.
    4. Tung Tung, Chi & Lin Chew, Kim, 1992. "A multicriteria Pareto-optimal path algorithm," European Journal of Operational Research, Elsevier, vol. 62(2), pages 203-209, October.
    5. Iori, Manuel & Martello, Silvano & Pretolani, Daniele, 2010. "An aggregate label setting policy for the multi-objective shortest path problem," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1489-1496, December.
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    Cited by:

    1. Pulido, Francisco Javier & Mandow, Lawrence & Pérez de la Cruz, José Luis, 2014. "Multiobjective shortest path problems with lexicographic goal-based preferences," European Journal of Operational Research, Elsevier, vol. 239(1), pages 89-101.
    2. Enrique Machuca & Lawrence Mandow, 2016. "Lower bound sets for biobjective shortest path problems," Journal of Global Optimization, Springer, vol. 64(1), pages 63-77, January.
    3. Zajac, Sandra & Huber, Sandra, 2021. "Objectives and methods in multi-objective routing problems: a survey and classification scheme," European Journal of Operational Research, Elsevier, vol. 290(1), pages 1-25.
    4. Duque, Daniel & Lozano, Leonardo & Medaglia, Andrés L., 2015. "An exact method for the biobjective shortest path problem for large-scale road networks," European Journal of Operational Research, Elsevier, vol. 242(3), pages 788-797.
    5. Perederieieva, Olga & Raith, Andrea & Schmidt, Marie, 2018. "Non-additive shortest path in the context of traffic assignment," European Journal of Operational Research, Elsevier, vol. 268(1), pages 325-338.

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