Approximate dynamic programming for stochastic resource allocation problems

Ali Forootani

doi:10.1109/JAS.2020.1003231

Approximate dynamic programming for stochastic resource allocation problems

2020-07-01

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DOI：10.1109/JAS.2020.1003231

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A stochastic resource allocation model, based on the principles of Markov decision processes (MDPs), is proposed in this paper. In particular, a general-purpose framework is developed, which takes into account resource requests for both instant and future needs. The considered framework can handle two types of reservations (i.e., specified and unspecified time interval reservation requests), and implement an overbooking business strategy to further increase business revenues. The resulting dynamic pricing problems can be regarded as sequential decision-making problems under uncertainty, which is solved by means of stochastic dynamic programming (DP) based algorithms. In this regard, Bellman's backward principle of optimality is exploited in order to provide all the implementation mechanisms for the proposed reservation pricing algorithm. The curse of dimensionality, as the inevitable issue of the DP both for instant resource requests and future resource reservations, occurs. In particular, an approximate dynamic programming (ADP) technique based on linear function approximations is applied to solve such scalability issues. Several examples are provided to show the effectiveness of the proposed approach.

Keyword:

Approximate dynamic programming (ADP)

dynamic programming (DP)

Markov decision processes (MDPs)

resource allocation problem

Approximate dynamic programming for stochastic resource allocation problems

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