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Collaborative Optimization of Storage Location Assignment and Path Planning in Robotic Mobile Fulfillment Systems

Author

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  • Jianming Cai

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
    Smart Transport Key Laboratory of Hunan Province, Changsha 410075, China)

  • Xiaokang Li

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
    Smart Transport Key Laboratory of Hunan Province, Changsha 410075, China)

  • Yue Liang

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Shan Ouyang

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

Abstract

The robotic mobile fulfillment system (RMFS) is a new automatic warehousing system, a type of green technology, and an emerging trend in the logistics industry. In this study, we take an RMFS as the research object and combine the connected issues of storage location assignment and path planning into one optimization problem from the perspective of collaborative optimization. A sustainable mathematical model for the collaborative optimization of storage location assignment and path planning (COSLAPP) is established, which considers the relationship between the location assignment of goods and rack storage and path planning in an RMFS. On this basis, we propose a location assignment strategy for goods clustering and rack turnover, which utilizes reservation tables, sets AGV operation rules to resolve AGV running conflicts, and improves the A-star(A*) algorithm based on the node load to find the shortest path by which the AGV handling the racks can complete the order picking. Ultimately, simulation studies were performed to ascertain the effectiveness of COSLAPP in the RMFS; the results show that the new approach can significantly improve order picking efficiency, reduce energy consumption, and lessen the operating costs of the warehouse of a distribution center.

Suggested Citation

  • Jianming Cai & Xiaokang Li & Yue Liang & Shan Ouyang, 2021. "Collaborative Optimization of Storage Location Assignment and Path Planning in Robotic Mobile Fulfillment Systems," Sustainability, MDPI, vol. 13(10), pages 1-26, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:10:p:5644-:d:556925
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    References listed on IDEAS

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    Cited by:

    1. Mohammed Alnahhal & Bashir Salah & Mohammed Ruzayqat, 2022. "An Efficient Approach to Investigate the Tradeoff between Double Handling and Needed Capacity in Automated Distribution Centers," Sustainability, MDPI, vol. 14(13), pages 1-25, June.
    2. Xuan Zhang & Tiantian Mo & Yougong Zhang, 2023. "Optimization of Storage Location Assignment for Non-Traditional Layout Warehouses Based on the Firework Algorithm," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
    3. Agnieszka A. Tubis & Honorata Poturaj, 2022. "Risk Related to AGV Systems—Open-Access Literature Review," Energies, MDPI, vol. 15(23), pages 1-23, November.
    4. Yi Li & Zhiyang Li, 2022. "Shuttle-Based Storage and Retrieval System: A Literature Review," Sustainability, MDPI, vol. 14(21), pages 1-18, November.
    5. Zhuang, Yanling & Zhou, Yun & Hassini, Elkafi & Yuan, Yufei & Hu, Xiangpei, 2022. "Rack retrieval and repositioning optimization problem in robotic mobile fulfillment systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    6. Mohammed Alnahhal & Bashir Salah & Rafiq Ahmad, 2022. "Increasing Throughput in Warehouses: The Effect of Storage Reallocation and the Location of Input/Output Station," Sustainability, MDPI, vol. 14(8), pages 1-16, April.

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