Abstract
In this study, we aim to develop a multiagent simulator for order picking in a logistics warehouse and to make it close to the actual data in the field. The product placement output by the optimization algorithm can be verified before the product placement is actually changed in the field, and more effective product placement can be proposed by using this simulator. The performance of the proposed simulator was evaluated based on the time from the creation of a product slip to the time when all the products in the slip are picked (survival time). It was confirmed that the simulation could be performed close to the actual survival time by using the actual data of 2020 in a real warehouse and selecting the appropriate parameters for each month.
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Acknowledgments
This work was supported in part by the Ministry of Education, Culture, Sports, Science and Technology-Japan, Grant–in–Aid for Scientific Research under grant #JP19H01137, #JP19H04025, #JP20H04018, #JP20J14182, and #JP20K19905. We are grateful to YAHATA NEJI Corporation for providing us with the real logistic operation data.
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Sakuma, T., Watanabe, M., Ihara, K., Kato, S. (2022). Development of a Multiagent Based Order Picking Simulator for Optimizing Operations in a Logistics Warehouse. In: Fujita, H., Fournier-Viger, P., Ali, M., Wang, Y. (eds) Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. IEA/AIE 2022. Lecture Notes in Computer Science(), vol 13343. Springer, Cham. https://doi.org/10.1007/978-3-031-08530-7_6
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DOI: https://doi.org/10.1007/978-3-031-08530-7_6
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