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    Stochastic Project Scheduling with Hierarchical Alternatives

    Access Status
    Fulltext not available
    Authors
    Tao, S.
    Wu, Changzhi
    Sheng, Z.
    Wang, Xiangyu
    Date
    2018
    Type
    Journal Article
    
    Metadata
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    Citation
    Tao, S. and Wu, C. and Sheng, Z. and Wang, X. 2018. Stochastic Project Scheduling with Hierarchical Alternatives. Applied Mathematical Modelling. 58: pp. 181-202.
    Source Title
    Applied Mathematical Modelling
    DOI
    10.1016/j.apm.2017.09.015
    ISSN
    0307-904X
    School
    School of Design and the Built Environment
    URI
    http://hdl.handle.net/20.500.11937/67034
    Collection
    • Curtin Research Publications
    Abstract

    © 2017 In this paper, a resource constrained project scheduling problem with hierarchical alternatives and stochastic activity durations is studied. A stochastic chance constraint is introduced to formulate this problem. A metaheuristic framework called SAA/DAAA through integrating the sampling average approximation (SAA) with the population-based evolutionary artificial algae algorithm (AAA) is developed to solve the problem due to the NP-hardness nature of the problem. The priority-selection list (PSL) and schedule generation scheme (SGS) are introduced for local search. Experiments with different sizes (50-scale, 100-scale, 150-scale) as well as different uncertainty levels (moderate, medium, high) are used as examples to illustrate and validate the proposed method. The influences of sample size, sampling times and confidence level are also analyzed during experiments. In addition, the proposed discrete AAA (DAAA) is compared with classic GA and numerical experiments show that the SAA/DAAA outperforms the SAA/GA in terms of both objectives and solving time.

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