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Solving Multimode Resource-Constrained Project Scheduling Problems Using an Organizational Evolutionary Algorithm

  • Conference paper
Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Volume 1

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 1))

Abstract

In this paper, a new evolutionary algorithm, namely organizational evolutionary algorithm for multimode resource-constrained project scheduling problems (OEA-MRCPSPs), is proposed. In OEA-MRCPSPs, the population is composed of organizations, and the number of organizations is adjusted by the splitting operator and annexing operator. In the evolutionary process of OEA-MRCPSPs, the global and local searches are combined efficiently. In the experiments, the performance of OEA-MRCPSPs is validated on benchmarks. The results show that OEA-MRCPSPs obtains a good performance in terms of both optimal solutions found and average deviations from optimal solutions or critical path lower bounds. The comparison results show that OEA-MRCPSPs outperforms six other existing algorithms.

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Author information

Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, 710071, China

    Lixia Wang & Jing Liu

Authors
  1. Lixia Wang
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  2. Jing Liu
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Editor information

Editors and Affiliations

  1. Department of Infomatics, Kindai University, Faculty of Science and Technology, Higashi-Osaka, Japan

    Hisashi Handa

  2. Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Osaka, Japan

    Hisao Ishibuchi

  3. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Yew-Soon Ong

  4. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

    Kay Chen Tan

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Wang, L., Liu, J. (2015). Solving Multimode Resource-Constrained Project Scheduling Problems Using an Organizational Evolutionary Algorithm. In: Handa, H., Ishibuchi, H., Ong, YS., Tan, K. (eds) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-13359-1_22

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  • DOI: https://doi.org/10.1007/978-3-319-13359-1_22

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13358-4

  • Online ISBN: 978-3-319-13359-1

  • eBook Packages: EngineeringEngineering (R0)

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