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Scenario-based stochastic resource allocation with uncertain probability parameters

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Abstract

The stochastic resource allocation (SRA) problem is an extensive class of combinatorial optimization problems widely existing in complex systems such as communication networks and unmanned systems. In SRA, the ability of a resource to complete a task is described by certain probability, and the objective is to maximize the reward by appropriately assigning available resources to different tasks. This paper is aimed at an important branch of SRA, that is, stochastic SRA (SSRA) for which the probability for resources to complete tasks is also uncertain. Firstly, a general SSRA model with multiple independent uncertain parameters (GSSRA-MIUP) is built to formulate the problem. Then, a scenario-based reformulation which can address multi-source uncertainties is proposed to facilitate the problem-solving process. Secondly, in view of the superiority of the differential evolution algorithm in real-valued optimization, a discrete version of this algorithm was originally proposed and further combined with a specialized local search to create an efficient hybrid optimizer. The hybrid algorithm is compared with the discrete differential evolution algorithm, a pure random sampling method, as well as a restart local search method. Experimental results show that the proposed hybrid optimizer has obvious advantages in solving GSSRA-MIUP problems.

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Authors and Affiliations

  1. School of Economics and Management, Beihang University, Beijing, 100191, China

    Guimei Fan & Haijun Huang

Authors
  1. Guimei Fan
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  2. Haijun Huang
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Correspondence to Guimei Fan.

Additional information

This research was supported by the National Natural Science Foundation of China under Grant No. 71361130011.

This paper was recommended for publication by Editor SUN Jian.

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Fan, G., Huang, H. Scenario-based stochastic resource allocation with uncertain probability parameters. J Syst Sci Complex 30, 357–377 (2017). https://doi.org/10.1007/s11424-017-6178-5

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  • DOI: https://doi.org/10.1007/s11424-017-6178-5

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