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A Relaxation-Based Pruning Technique for a Class of Stochastic Shortest Path Problems

Author

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  • Ishwar Murthy

    (Department of Information Systems and Decision Sciences, Louisiana State University, Baton Rouge, Louisiana 70803)

  • Sumit Sarkar

    (Department of Information Systems and Decision Sciences, Louisiana State University, Baton Rouge, Louisiana 70803)

Abstract

In this paper a form of the stochastic shortest path problem is considered where the optimal path is one that maximizes the expected utility which is concave and quadratic. The principal contribution of this paper is the development of a relaxation based pruning technique which is incorporated into a label setting procedure. The basic label setting procedure solves the problem by generating all Pareto-optimal paths. However, the number of such paths can grow exponentially with the size of the problem. The relaxation based pruning technique developed here is able to recognize and discard most of the Pareto-optimal paths that do not contribute to the optimal path. Our computational results show that the label setting procedure that incorporates the pruning technique consistently outperforms the basic label setting procedure, and is able to solve large problems very quickly.

Suggested Citation

  • Ishwar Murthy & Sumit Sarkar, 1996. "A Relaxation-Based Pruning Technique for a Class of Stochastic Shortest Path Problems," Transportation Science, INFORMS, vol. 30(3), pages 220-236, August.
    • Handle: RePEc:inm:ortrsc:v:30:y:1996:i:3:p:220-236
    DOI: 10.1287/trsc.30.3.220
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    Cited by:

    1. Levering, Nikki & Boon, Marko & Mandjes, Michel & Núñez-Queija, Rudesindo, 2022. "A framework for efficient dynamic routing under stochastically varying conditions," Transportation Research Part B: Methodological, Elsevier, vol. 160(C), pages 97-124.
    2. Huang, He & Gao, Song, 2012. "Optimal paths in dynamic networks with dependent random link travel times," Transportation Research Part B: Methodological, Elsevier, vol. 46(5), pages 579-598.
    3. Fu, Liping & Rilett, L. R., 1998. "Expected shortest paths in dynamic and stochastic traffic networks," Transportation Research Part B: Methodological, Elsevier, vol. 32(7), pages 499-516, September.
    4. Fu, Liping, 2001. "An adaptive routing algorithm for in-vehicle route guidance systems with real-time information," Transportation Research Part B: Methodological, Elsevier, vol. 35(8), pages 749-765, September.
    5. Yu Nie & Xing Wu & Tito Homem-de-Mello, 2012. "Optimal Path Problems with Second-Order Stochastic Dominance Constraints," Networks and Spatial Economics, Springer, vol. 12(4), pages 561-587, December.
    6. Elise D. Miller-Hooks & Hani S. Mahmassani, 2000. "Least Expected Time Paths in Stochastic, Time-Varying Transportation Networks," Transportation Science, INFORMS, vol. 34(2), pages 198-215, May.
    7. Nie, Yu (Marco) & Wu, Xing & Dillenburg, John F. & Nelson, Peter C., 2012. "Reliable route guidance: A case study from Chicago," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(2), pages 403-419.
    8. Murthy, Ishwar & Sarkar, Sumit, 1997. "Exact algorithms for the stochastic shortest path problem with a decreasing deadline utility function," European Journal of Operational Research, Elsevier, vol. 103(1), pages 209-229, November.
    9. Shahabi, Mehrdad & Unnikrishnan, Avinash & Boyles, Stephen D., 2013. "An outer approximation algorithm for the robust shortest path problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 58(C), pages 52-66.
    10. Leilei Zhang & Tito Homem-de-Mello, 2017. "An Optimal Path Model for the Risk-Averse Traveler," Transportation Science, INFORMS, vol. 51(2), pages 518-535, May.
    11. Ishwar Murthy & Sumit Sarkar, 1998. "Stochastic Shortest Path Problems with Piecewise-Linear Concave Utility Functions," Management Science, INFORMS, vol. 44(11-Part-2), pages 125-136, November.
    12. Yang, Lixing & Zhang, Yan & Li, Shukai & Gao, Yuan, 2016. "A two-stage stochastic optimization model for the transfer activity choice in metro networks," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 271-297.
    13. Zhang, Yuli & Shen, Zuo-Jun Max & Song, Shiji, 2016. "Parametric search for the bi-attribute concave shortest path problem," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 150-168.
    14. Axel Parmentier, 2019. "Algorithms for non-linear and stochastic resource constrained shortest path," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 89(2), pages 281-317, April.
    15. Nie, Yu (Marco) & Wu, Xing, 2009. "Shortest path problem considering on-time arrival probability," Transportation Research Part B: Methodological, Elsevier, vol. 43(6), pages 597-613, July.

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