Asymptotic analysis for multi-objective sequential stochastic assignment problems

G. Yu, S. H. Jacobson, N. Kiyavash

Research output: Contribution to journalArticle


We provide an asymptotic analysis of multi-objective sequential stochastic assignment problems (MOSSAP). In MOSSAP, a fixed number of tasks arrive sequentially, with an n-dimensional value vector revealed upon arrival. Each task is assigned to one of a group of known workers immediately upon arrival, with the reward given by an n-dimensional product-form vector. The objective is to maximize each component of the expected reward vector. We provide expressions for the asymptotic expected reward per task for each component of the reward vector and compare the convergence rates for three classes of Pareto optimal policies.

Original languageEnglish (US)
Pages (from-to)223-264
Number of pages42
Issue number2
StatePublished - Feb 17 2020



  • Multi-objective sequential stochastic assignment problems
  • Pareto optimal policies
  • asymptotic analysis
  • convergence rate

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation

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