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The resource allocation model for multi-process instances based on particle swarm optimization

Author

Listed:
  • Weidong Zhao

    (Fudan University)

  • Qingfeng Zeng

    (Shanghai University of Finance & Economics)

  • Guangjian Zheng

    (Fudan University)

  • Liu Yang

    (Fudan University)

Abstract

Resource allocation in process management focuses on how to maximize process performance via proper resource allocation since the quality of resource allocation determines process outcome. In order to improve resource allocation, this paper proposes a resource allocation method, which is based on the improved hybrid particle swarm optimization (PSO) in the multi-process instance environment. Meanwhile, a new resource allocation model is put forward, which can optimize the resource allocation problem reasonably. Furthermore, some improvements are made to streamline the effectiveness of the method, so as to enhance resource scheduling results. In the end, experiments are conducted to demonstrate the effectiveness of the proposed method.

Suggested Citation

  • Weidong Zhao & Qingfeng Zeng & Guangjian Zheng & Liu Yang, 0. "The resource allocation model for multi-process instances based on particle swarm optimization," Information Systems Frontiers, Springer, vol. 0, pages 1-10.
    • Handle: RePEc:spr:infosf:v::y::i::d:10.1007_s10796-017-9743-5
    DOI: 10.1007/s10796-017-9743-5
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    Cited by:

    1. Tamal Mondal & Prithviraj Pramanik & Indrajit Bhattacharya & Naiwrita Boral & Saptarshi Ghosh, 2018. "Analysis and Early Detection of Rumors in a Post Disaster Scenario," Information Systems Frontiers, Springer, vol. 20(5), pages 961-979, October.
    2. Vijayan Sugumaran & T. V. Geetha & D. Manjula & Hema Gopal, 2017. "Guest Editorial: Computational Intelligence and Applications," Information Systems Frontiers, Springer, vol. 19(5), pages 969-974, October.

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