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Rack shape and energy efficient operations in automated storage and retrieval systems

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  • Antonella Meneghetti
  • Eleonora Dal Borgo
  • Luca Monti

Abstract

Energy efficiency has become a primary goal to be pursued for sustainable logistics. In automated storage and retrieval systems this leads to revise the traditional control policies aimed at picking time minimisation and to pay more attention to rack configuration, which has been not a research concern from the time-based perspective. Proper models for energy calculation should be developed by introducing new factors neglected in time analysis, such as the weight of unit loads and the differentiation of shifts along the horizontal and vertical axis as regard energy requirements, due to different contribution of gravity, inertia and friction. In this study, a classification of racks based on system height is proposed in order to select the proper crane specifications needed to compute the torque to be overcome by motors to serve a given location within a rack. An overall optimisation model based on Constraint Programming hybridised with Large Neighborhood Search is developed, allowing the joint application of the best control policies for storage assignment and sequencing both for time and energy-based optimisation, as well as the introduction of multiple weight unit loads and energy recovery. Simulations analysis is performed in order to assess the impact of the rack shape on energy saving. Results show how, regardless the demand curve and the optimisation objective, the best performances in terms of energy efficiency are reached by the intermediate height rack shapes, while the lower ones outperform when considering travel time performance.

Suggested Citation

  • Antonella Meneghetti & Eleonora Dal Borgo & Luca Monti, 2015. "Rack shape and energy efficient operations in automated storage and retrieval systems," International Journal of Production Research, Taylor & Francis Journals, vol. 53(23), pages 7090-7103, December.
  • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:23:p:7090-7103
    DOI: 10.1080/00207543.2015.1008107
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    Cited by:

    1. Bortolini, Marco & Faccio, Maurizio & Ferrari, Emilio & Gamberi, Mauro & Pilati, Francesco, 2017. "Time and energy optimal unit-load assignment for automatic S/R warehouses," International Journal of Production Economics, Elsevier, vol. 190(C), pages 133-145.
    2. Chen, Gang & Feng, Haolin & Luo, Kaiyi & Tang, Yanli, 2021. "Retrieval-oriented storage relocation optimization of an automated storage and retrieval system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    3. Sara Perotti & Lorenzo Bruno Prataviera & Marco Melacini, 2022. "Assessing the environmental impact of logistics sites through CO2eq footprint computation," Business Strategy and the Environment, Wiley Blackwell, vol. 31(4), pages 1679-1694, May.
    4. Antonella Meneghetti & Fabio Dal Magro & Patrizia Simeoni, 2018. "Fostering Renewables into the Cold Chain: How Photovoltaics Affect Design and Performance of Refrigerated Automated Warehouses," Energies, MDPI, vol. 11(5), pages 1-20, April.
    5. Li, Xiaowei & Hua, Guowei & Huang, Anqiang & Sheu, Jiuh-Biing & Cheng, T.C.E. & Huang, Fengquan, 2020. "Storage assignment policy with awareness of energy consumption in the Kiva mobile fulfilment system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    6. Raffaele Carli & Mariagrazia Dotoli & Salvatore Digiesi & Francesco Facchini & Giorgio Mossa, 2020. "Sustainable Scheduling of Material Handling Activities in Labor-Intensive Warehouses: A Decision and Control Model," Sustainability, MDPI, vol. 12(8), pages 1-25, April.
    7. Yi Li & Zhiyang Li, 2022. "Shuttle-Based Storage and Retrieval System: A Literature Review," Sustainability, MDPI, vol. 14(21), pages 1-18, November.
    8. Polten, Lukas & Emde, Simon, 2022. "Multi-shuttle crane scheduling in automated storage and retrieval systems," European Journal of Operational Research, Elsevier, vol. 302(3), pages 892-908.
    9. Tamás Bányai, 2018. "Real-Time Decision Making in First Mile and Last Mile Logistics: How Smart Scheduling Affects Energy Efficiency of Hyperconnected Supply Chain Solutions," Energies, MDPI, vol. 11(7), pages 1-25, July.
    10. Hyun-woo Jeon & Ahmad Ebrahimi & Ga-hyun Lee, 2023. "A Simulation-Based Experimental Design for Analyzing Energy Consumption and Order Tardiness in Warehousing Systems," Sustainability, MDPI, vol. 15(20), pages 1-25, October.
    11. 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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