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Simulation-based regression analysis for the replenishment strategy of the crane & shuttle-based storage and retrieval system

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Abstract

This research focuses on big experimental data analysis and the replenishment operation in a crane & shuttle based storage and retrieval system (C&SBS/RS). It is proposed that the order structure is expressed by three indicators: the order strength, the order size, and the order density. This aims to develop a regression function for the replenishment strategy to reveal the relationship between the order structure and replenishment parameters. The simulation model of the system is developed using FlexSim 7.1. A total of 13,440 combinations of order structures are provided as the simulation input. The statistical analyses are completed using the popular statistical software MINITAB. In this study, we use two different methods, best subset regression, and stepwise regression, to fit the regression function and find the best regression. According to the regression function, operation managers can develop better operation strategies.

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Funding

This research was supported by the Natural Science Foundation of Shandong Province (No. ZR2020MF085)

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Authors and Affiliations

  1. School of Control Science and Engineering, Shandong University, Jinan, 250061, Shandong, China

    Wenkai Ma, Jinchang Hu, Yanyan Wang & Yaohua Wu

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  1. Wenkai Ma
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  2. Jinchang Hu
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  3. Yanyan Wang
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  4. Yaohua Wu
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Correspondence to Yaohua Wu.

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Appendix

Appendix

When the replenishment transaction arrives, CBS/RS processes the pallet outbound and pallet inbound, while SBS/RS processes the empty tote outbound and tote inbound. The flowchart is shown in Figs. 5, 6, 7 and 8.

Fig. 5
figure 5

Pallet outbound in the CBS/RS

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Fig. 6
figure 6

Pallet inbound in the CBS/RS

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Fig. 7
figure 7

Empty tote outbound in the SBS/RS

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Fig. 8
figure 8

Empty tote inbound in the SBS/RS

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Ma, W., Hu, J., Wang, Y. et al. Simulation-based regression analysis for the replenishment strategy of the crane & shuttle-based storage and retrieval system. Cluster Comput 25, 77–89 (2022). https://doi.org/10.1007/s10586-021-03372-7

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  • DOI: https://doi.org/10.1007/s10586-021-03372-7

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