Publisher： Cambridge University Press
E-ISSN： 1475-6072|53|4|1052-1063
ISSN： 0021-9002
Source： Journal of Applied Probability, Vol.53, Iss.4, 2016-12, pp. : 1052-1063
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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.
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