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arXiv · 2106.04944

Non-Parametric Stochastic Sequential Assignment With Random Arrival Times

Danial Dervovic·Parisa Hassanzadeh·Samuel Assefa·Prashant Reddy
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Abstract

We consider a problem wherein jobs arrive at random times and assume random values. Upon each job arrival, the decision-maker must decide immediately whether or not to accept the job and gain the value on offer as a reward, with the constraint that they may only accept at most $n$ jobs over some reference time period. The decision-maker only has access to $M$ independent realisations of the job arrival process. We propose an algorithm, Non-Parametric Sequential Allocation (NPSA), for solving this problem. Moreover, we prove that the expected reward returned by the NPSA algorithm converges in probability to optimality as $M$ grows large. We demonstrate the effectiveness of the algorithm empirically on synthetic data and on public fraud-detection datasets, from where the motivation for this work is derived.

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Danial Dervovic, Parisa Hassanzadeh, Samuel Assefa, Prashant Reddy. 2021-06-09. Non-Parametric Stochastic Sequential Assignment With Random Arrival Times. https://doi.org/10.24963/ijcai.2021%2F579

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