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Gathering Strength, Gathering Storms: Knowledge Transfer via Selection for VRPTW

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  • Wendi Xu

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Shenyang 110819, China
    Frontier Science Center for Industrial Intelligence and System Optimization, Northeastern University, Shenyang 110819, China)

  • Xianpeng Wang

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Shenyang 110819, China)

  • Qingxin Guo

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    Frontier Science Center for Industrial Intelligence and System Optimization, Northeastern University, Shenyang 110819, China)

  • Xiangman Song

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Shenyang 110819, China
    Frontier Science Center for Industrial Intelligence and System Optimization, Northeastern University, Shenyang 110819, China)

  • Ren Zhao

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Shenyang 110819, China
    Frontier Science Center for Industrial Intelligence and System Optimization, Northeastern University, Shenyang 110819, China)

  • Guodong Zhao

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Shenyang 110819, China
    Frontier Science Center for Industrial Intelligence and System Optimization, Northeastern University, Shenyang 110819, China)

  • Yang Yang

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Shenyang 110819, China
    Frontier Science Center for Industrial Intelligence and System Optimization, Northeastern University, Shenyang 110819, China)

  • Te Xu

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Shenyang 110819, China
    Frontier Science Center for Industrial Intelligence and System Optimization, Northeastern University, Shenyang 110819, China)

  • Dakuo He

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

Abstract

Recently, due to the growth in machine learning and data mining, for scheduling applications in China’s industrial intelligence, we are quite fortunate to witness a paradigm of evolutionary scheduling via learning, which includes a new tool of evolutionary transfer optimization (ETO). As a new subset in ETO, single-objective to multi-objective/many-objective optimization (SMO) acts as a powerful, abstract and general framework with wide industrial applications like shop scheduling and vehicle routing. In this paper, we focus on the general mechanism of selection that selects or gathers elite and high potential solutions towards gathering/transferring strength from single-objective problems, or gathering/transferring storms of knowledge from solved tasks. Extensive studies in vehicle routing problems with time windows (VRPTW) on well-studied benchmarks validate the great universality of the SMO framework. Our investigations (1) contribute to a deep understanding of SMO, (2) enrich the classical and fundamental theory of building blocks for genetic algorithms and memetic algorithms, and (3) provide a completive and potential solution for VRPTW.

Suggested Citation

  • Wendi Xu & Xianpeng Wang & Qingxin Guo & Xiangman Song & Ren Zhao & Guodong Zhao & Yang Yang & Te Xu & Dakuo He, 2022. "Gathering Strength, Gathering Storms: Knowledge Transfer via Selection for VRPTW," Mathematics, MDPI, vol. 10(16), pages 1-17, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2888-:d:886509
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    References listed on IDEAS

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    1. Jean-Paul Watson & Laura Barbulescu & L. Darrell Whitley & Adele E. Howe, 2002. "Contrasting Structured and Random Permutation Flow-Shop Scheduling Problems: Search-Space Topology and Algorithm Performance," INFORMS Journal on Computing, INFORMS, vol. 14(2), pages 98-123, May.
    2. Ruiz, Rubén & Pan, Quan-Ke & Naderi, Bahman, 2019. "Iterated Greedy methods for the distributed permutation flowshop scheduling problem," Omega, Elsevier, vol. 83(C), pages 213-222.
    3. Ying-ping Chen & Chung-Yao Chuang & Yuan-Wei Huang, 2012. "Inductive linkage identification on building blocks of different sizes and types," International Journal of Systems Science, Taylor & Francis Journals, vol. 43(12), pages 2202-2213.
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    Cited by:

    1. Kangye Tan & Weihua Liu & Fang Xu & Chunsheng Li, 2023. "Optimization Model and Algorithm of Logistics Vehicle Routing Problem under Major Emergency," Mathematics, MDPI, vol. 11(5), pages 1-18, March.

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