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An Integrated Implementation Framework for Warehouse 4.0 Based on Inbound and Outbound Operations

Author

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  • Jizhuang Hui

    (Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an 710064, China
    Institute of Smart Manufacturing Systems, Chang’an University, Xi’an 710064, China)

  • Shaowei Zhi

    (Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an 710064, China
    Institute of Smart Manufacturing Systems, Chang’an University, Xi’an 710064, China)

  • Weichen Liu

    (Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an 710064, China
    Institute of Smart Manufacturing Systems, Chang’an University, Xi’an 710064, China)

  • Changhao Chu

    (Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an 710064, China
    Institute of Smart Manufacturing Systems, Chang’an University, Xi’an 710064, China)

  • Fuqiang Zhang

    (Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an 710064, China
    Institute of Smart Manufacturing Systems, Chang’an University, Xi’an 710064, China)

Abstract

Warehouse 4.0 adopts automation, IoT, and big data technologies to establish an intelligent warehousing system for efficient, real-time management of storage, handling, and picking. Addressing challenges like unreasonable storage allocation and inefficient order fulfillment, this paper presents an integrated framework that utilizes swarm intelligence algorithms and collaborative scheduling strategies to optimize inbound/outbound operations. First, for inbound processes, an algorithm-driven storage allocation model is proposed to solve stacker crane scheduling problems. Then, for outbound operations, a “1+N+M” mathematical model is developed, optimized through a three-stage algorithm addressing order picking and distribution scheduling. Finally, a case study of an industrial warehouse validates the proposed methods. The improved mayfly algorithm demonstrates excellent performance, achieving 64.5–74.5% faster convergence and 20.1–24.7% lower fitness values compared to traditional algorithms. The three-stage approach reduces order fulfillment time by 12% and average processing time by 1.8% versus conventional methods. These results confirm the framework’s effectiveness in enhancing warehouse operational efficiency through intelligent automation and optimized resource scheduling.

Suggested Citation

  • Jizhuang Hui & Shaowei Zhi & Weichen Liu & Changhao Chu & Fuqiang Zhang, 2025. "An Integrated Implementation Framework for Warehouse 4.0 Based on Inbound and Outbound Operations," Mathematics, MDPI, vol. 13(14), pages 1-26, July.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:14:p:2276-:d:1701964
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    References listed on IDEAS

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