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Sequential stochastic assignment problem with time-dependent random success rates

Part of: Discrete mathematics in relation to computer science Stochastic processes

Published online by Cambridge University Press:  09 December 2016

Golshid Baharian ,
Arash Khatibi  and
Sheldon H. Jacobson
Golshid Baharian*
Affiliation:
University of Montreal
Arash Khatibi*
Affiliation:
University of Illinois at Urbana-Champaign
Sheldon H. Jacobson*
Affiliation:
University of Illinois at Urbana-Champaign
*
* Postal address: CHU Sainte Justine, University of Montreal, 3175 Chemin de la Cote Sainte-Catherine, Montreal, Québec, H3T 1C5, Canada. Email address: [email protected]
** Postal address: Department of Computer Science, University of Illinois at Urbana-Champaign, 201 North Goodwin Avenue, Urbana, IL 61801, USA.
** Postal address: Department of Computer Science, University of Illinois at Urbana-Champaign, 201 North Goodwin Avenue, Urbana, IL 61801, USA.
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Abstract

The sequential stochastic assignment problem (SSAP) allocates distinct workers with deterministic values to sequentially arriving tasks with stochastic parameters to maximize the expected total reward. In this paper we study an extension of the SSAP, in which the worker values are considered to be random variables, taking on new values upon each task arrival. Several SSAP models with different assumptions on the distribution of the worker values and closed-form expressions for optimal assignment policies are presented.

Keywords

Sequential stochastic assignmentoptimal policy

MSC classification

Primary: 60G20: Generalized stochastic processes
Secondary: 68R01: General
Type
Research Papers
Information
Journal of Applied Probability , Volume 53 , Issue 4 , December 2016 , pp. 1052 - 1063
Copyright
Copyright © Applied Probability Trust 2016 

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