Path finding under uncertainty

Anthony Chen, Zhaowang Ji

Research output: Journal article publicationJournal articleAcademic researchpeer-review

104 Citations (Scopus)


Path finding problems have many real-world applications in various fields, such as operations research, computer science, telecommunication, transportation, etc. In this paper, we examine three definitions of optimality for finding the optimal path under an uncertain environment. These three stochastic path finding models are formulated as the expected value model, dependent-chance model, and chance-constrained model using different criteria to hedge against the travel time uncertainty. A simulation-based genetic algorithm procedure is developed to solve these path finding models under uncertainties. Numerical results are also presented to demonstrate the features of these stochastic path finding models.
Original languageEnglish
Pages (from-to)19-37
Number of pages19
JournalJournal of Advanced Transportation
Issue number1
Publication statusPublished - 1 Jan 2005
Externally publishedYes

ASJC Scopus subject areas

  • Automotive Engineering
  • Economics and Econometrics
  • Mechanical Engineering
  • Computer Science Applications
  • Strategy and Management

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