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

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Publicado no:Mathematics vol. 13, no. 14 (2025), p. 2276-2302
Autor principal: Jizhuang, Hui
Outros Autores: Zhi Shaowei, Liu, Weichen, Chu Changhao, Zhang, Fuqiang
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MDPI AG
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100 1 |a Jizhuang, Hui  |u Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an 710064, China; huijz@chd.edu.cn (J.H.); 2021025006@chd.edu.cn (S.Z.); 2023025005@chd.edu.cn (W.L.); 2020125046@chd.edu.cn (C.C.) 
245 1 |a An Integrated Implementation Framework for Warehouse 4.0 Based on Inbound and Outbound Operations 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a 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. 
653 |a Swarm intelligence 
653 |a Cranes 
653 |a Collaboration 
653 |a Time management 
653 |a Order quantity 
653 |a Automation 
653 |a Order picking 
653 |a Queuing theory 
653 |a Internet of Things 
653 |a Efficiency 
653 |a Order processing 
653 |a Scheduling 
653 |a Costs 
653 |a Genetic algorithms 
653 |a Resource scheduling 
653 |a Algorithms 
653 |a Supply chains 
653 |a Linear programming 
653 |a Real time 
653 |a Logistics 
653 |a Warehouses 
653 |a Optimization algorithms 
653 |a Inventory 
700 1 |a Zhi Shaowei  |u Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an 710064, China; huijz@chd.edu.cn (J.H.); 2021025006@chd.edu.cn (S.Z.); 2023025005@chd.edu.cn (W.L.); 2020125046@chd.edu.cn (C.C.) 
700 1 |a Liu, Weichen  |u Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an 710064, China; huijz@chd.edu.cn (J.H.); 2021025006@chd.edu.cn (S.Z.); 2023025005@chd.edu.cn (W.L.); 2020125046@chd.edu.cn (C.C.) 
700 1 |a Chu Changhao  |u Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an 710064, China; huijz@chd.edu.cn (J.H.); 2021025006@chd.edu.cn (S.Z.); 2023025005@chd.edu.cn (W.L.); 2020125046@chd.edu.cn (C.C.) 
700 1 |a Zhang, Fuqiang  |u Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an 710064, China; huijz@chd.edu.cn (J.H.); 2021025006@chd.edu.cn (S.Z.); 2023025005@chd.edu.cn (W.L.); 2020125046@chd.edu.cn (C.C.) 
773 0 |t Mathematics  |g vol. 13, no. 14 (2025), p. 2276-2302 
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