Asymptotic analysis for multi-objective sequential stochastic assignment problems

G. Yu, Sheldon Howard Jacobson, N. Kiyavash

Research output: Contribution to journalArticle

Abstract

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)
JournalStochastics
DOIs
StatePublished - Jan 1 2019

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Asymptotic analysis
Assignment Problem
Reward
Asymptotic Analysis
n-dimensional
Product Form
Optimal Policy
Immediately
Convergence Rate
Maximise

Keywords

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

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation

Cite this

Asymptotic analysis for multi-objective sequential stochastic assignment problems. / Yu, G.; Jacobson, Sheldon Howard; Kiyavash, N.

In: Stochastics, 01.01.2019.

Research output: Contribution to journalArticle

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