New reformulations of distributionally robust shortest path problem

Jianqiang Cheng, Janny Leung, Abdel Lisser

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


This paper considers a stochastic version of the shortest path problem, namely the Distributionally Robust Stochastic Shortest Path Problem (DRSSPP) on directed graphs. In this model, each arc has a deterministic cost and a random delay. The mean vector and the second-moment matrix of the uncertain data are assumed to be known, but the exact information of the distribution is unknown. A penalty occurs when the given delay constraint is not satisfied. The objective is to minimize the sum of the path cost and the expected path delay penalty. As this problem is NP-hard, we propose new reformulations and approximations using a sequence of semidefinite programming problems which provide tight lower bounds. Finally, numerical tests are conducted to illustrate the tightness of the bounds and the value of the proposed distributionally robust approach.

Original languageEnglish (US)
Pages (from-to)196-204
Number of pages9
JournalComputers and Operations Research
StatePublished - Oct 1 2016
Externally publishedYes


  • Distributionally robust optimization
  • Semidefinite programming
  • Shortest path
  • Stochastic programming

ASJC Scopus subject areas

  • General Computer Science
  • Modeling and Simulation
  • Management Science and Operations Research


Dive into the research topics of 'New reformulations of distributionally robust shortest path problem'. Together they form a unique fingerprint.

Cite this