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Fast GA-based project scheduling for computing resources allocation in a cloud manufacturing system

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  • Yang-Kuei Lin

    (Feng Chia University)

  • Chin Soon Chong

    (A-star)

Abstract

Cloud manufacturing is becoming an increasingly popular enterprise model in which computing resources are made available on-demand to the user as needed. Cloud manufacturing aims at providing low-cost, resource-sharing and effective coordination. In this study, we present a genetic algorithm (GA) based resource constraint project scheduling, incorporating a number of new ideas (enhancements and local search) for solving computing resources allocation problems in a cloud manufacturing system. A newly generated offspring may not be feasible due to task precedence and resource availability constraints. Conflict resolutions and enhancements are performed on newly generated offsprings after crossover or mutation. The local search can exploit the neighborhood of solutions to find better schedules. Due to its complex characteristics, computing resources allocation in a cloud manufacturing system is NP-hard. Computational results show that the proposed GA can rapidly provide a good quality schedule that can optimally allocate computing resources and satisfy users’ demands.

Suggested Citation

  • Yang-Kuei Lin & Chin Soon Chong, 2017. "Fast GA-based project scheduling for computing resources allocation in a cloud manufacturing system," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1189-1201, June.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:5:d:10.1007_s10845-015-1074-0
    DOI: 10.1007/s10845-015-1074-0
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    Cited by:

    1. Asma Talhi & Virginie Fortineau & Jean-Charles Huet & Samir Lamouri, 2019. "Ontology for cloud manufacturing based Product Lifecycle Management," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2171-2192, June.
    2. Haghnegahdar, Lida & Chen, Yu & Wang, Yong, 2022. "Enhancing dynamic energy network management using a multiagent cloud-fog structure," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    3. Baodong Li & Yu Yang & Jiafu Su & Zhichao Liang & Sheng Wang, 2020. "Two-sided matching decision-making model with hesitant fuzzy preference information for configuring cloud manufacturing tasks and resources," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2033-2047, December.
    4. Shiyong Yin & Jinsong Bao & Jie Zhang & Jie Li & Junliang Wang & Xiaodi Huang, 2020. "Real-time task processing for spinning cyber-physical production systems based on edge computing," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2069-2087, December.
    5. Yankai Wang & Shilong Wang & Bo Yang & Bo Gao & Sibao Wang, 2022. "An effective adaptive adjustment method for service composition exception handling in cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 735-751, March.
    6. Wang Shijie & Zhang Yingfeng, 2021. "A credit-based dynamical evaluation method for the smart configuration of manufacturing services under Industrial Internet of Things," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1091-1115, April.
    7. Shuai Zhang & Yangbing Xu & Wenyu Zhang & Dejian Yu, 2019. "A new fuzzy QoS-aware manufacture service composition method using extended flower pollination algorithm," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2069-2083, June.
    8. Di Liang & Jieyi Wang & Ran Bhamra & Liezhao Lu & Yuting Li, 2022. "A Multi-Service Composition Model for Tasks in Cloud Manufacturing Based on VS–ABC Algorithm," Mathematics, MDPI, vol. 10(21), pages 1-24, October.

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