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dc.contributor.authorTao, S.
dc.contributor.authorWu, Changzhi
dc.contributor.authorSheng, Z.
dc.contributor.authorWang, Xiangyu
dc.date.accessioned2018-05-18T07:57:02Z
dc.date.available2018-05-18T07:57:02Z
dc.date.created2018-05-18T00:22:56Z
dc.date.issued2018
dc.identifier.citationTao, S. and Wu, C. and Sheng, Z. and Wang, X. 2018. Stochastic Project Scheduling with Hierarchical Alternatives. Applied Mathematical Modelling. 58: pp. 181-202.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/67034
dc.identifier.doi10.1016/j.apm.2017.09.015
dc.description.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.

dc.publisherElsevier
dc.titleStochastic Project Scheduling with Hierarchical Alternatives
dc.typeJournal Article
dcterms.source.volume58
dcterms.source.startPage181
dcterms.source.endPage202
dcterms.source.issn0307-904X
dcterms.source.titleApplied Mathematical Modelling
curtin.departmentSchool of Design and the Built Environment
curtin.accessStatusFulltext not available


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