Abstract
Given a finite number of workers with constant success rates, the Sequential Stochastic Assignment problem (SSAP) assigns the workers to sequentially arriving tasks with independent and identically distributed reward values, so as to maximize the total expected reward. This article studies the SSAP, with some (or all) workers having random success rates that are assumed to be independent but not necessarily identically distributed. Several assignment policies are proposed to address different levels of uncertainty in the success rates. Specifically, if the probability density functions of the random success rates are known, an optimal mixed policy is provided. If only the expected values of these rates are known, an optimal expectation policy is derived.
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