Asymptotic analysis for multi-objective sequential stochastic assignment problems. Issue 2 (17th February 2020)

• Record Type: Journal Article
• Authors: Yu, G.; Jacobson, S. H.; Kiyavash, N.
• Is Part Of: Stochastics. Volume 92:Issue 2(2020)
• Page Start: 223
• Page End: 264
• Publication Date: 2020-02-17
• Subjects: Multi-objective sequential stochastic assignment problems -- asymptotic analysis -- convergence rate -- Pareto optimal policies; Stochastic processes -- Periodicals; Probabilities -- Periodicals; 519.2
• DOI: 10.1080/17442508.2019.1612898
• Languages: English
• ISSNs: 1744-2508

Abstract

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.