{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:13:37Z","timestamp":1779203617757,"version":"3.51.4"},"reference-count":21,"publisher":"Fuji Technology Press Ltd.","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JACIII","J. Adv. Comput. Intell. Intell. Inform."],"published-print":{"date-parts":[[2026,5,20]]},"abstract":"<jats:p>The tobacco high-bay warehouse, functioning as a buffer warehouse in the cigarette production process, experiences significant fluctuations in space utilization and inbound\/outbound operations efficiency due to changes in order types and batch sizes. With its high throughput, improving the warehouse efficiency is a critical issue that needs to be addressed. This paper proposes an integrated optimization method for dynamic storage allocation and inbound\/outbound operation scheduling, with the goal of minimizing the completion time of batch inbound and outbound tasks. A dynamic slot allocation model for the overhead storage is developed, and a method is introduced to simultaneously consider both outbound slots and available slots as preferred options for inbound slot selection. To ensure that slots remain vacant during the inbound process, a penalty function is incorporated into the improved genetic algorithm, guaranteeing that inbound operations are only executed when a slot is vacant. The selection of outbound slots is optimized using relative distance, and task sequencing is improved to enhance the efficiency of inbound and outbound operations, reducing stacker crane operation time. The algorithm is applied under different operational conditions. Experimental results show that the proposed integrated optimization method significantly improves the operational efficiency of the overhead storage system. Additionally, the optimized inbound task sequence reduces task completion times and minimizes delays.<\/jats:p>","DOI":"10.20965\/jaciii.2026.p0738","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:02:09Z","timestamp":1779202929000},"page":"738-748","source":"Crossref","is-referenced-by-count":0,"title":["Integrated Optimization of Dynamic Storage Allocation and Task Scheduling for Batch In\/Out Operation in Tobacco High-Bay Warehouse"],"prefix":"10.20965","volume":"30","author":[{"given":"Wenjuan","family":"Wang","sequence":"first","affiliation":[{"name":"China Tobacco Zhejiang Industrial Co., Ltd., No.2001 Jiapuxi Road, Fenghua, Ningbo, Zhejiang 315504, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaolong","family":"Wu","sequence":"additional","affiliation":[{"name":"China Tobacco Zhejiang Industrial Co., Ltd., No.2001 Jiapuxi Road, Fenghua, Ningbo, Zhejiang 315504, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengting","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Tobacco Zhejiang Industrial Co., Ltd., No.2001 Jiapuxi Road, Fenghua, Ningbo, Zhejiang 315504, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dingke","family":"Shi","sequence":"additional","affiliation":[{"name":"China Tobacco Zhejiang Industrial Co., Ltd., No.2001 Jiapuxi Road, Fenghua, Ningbo, Zhejiang 315504, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junhe","family":"Yu","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering and Mechanics, Ningbo University, 818 Fenghua Road, Jiangbei, Ningbo, Zhejiang 315000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"8550","published-online":{"date-parts":[[2026,5,20]]},"reference":[{"key":"key-10.20965\/jaciii.2026.p0738-1","doi-asserted-by":"crossref","unstructured":"L. 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Wintz, \u201cDuration-of-Stay Storage Assignment under Uncertainty,\u201d Int. Conf. on Learning Representations (ICLR), 2020."},{"key":"key-10.20965\/jaciii.2026.p0738-18","unstructured":"A. Rim\u00e9l\u00e9, P. Grangier, M. Gamache et al., \u201cE-commerce warehousing: Learning a storage policy,\u201d arXiv:2101.08828, 2021. https:\/\/doi.org\/10.48550\/arXiv.2101.08828"},{"key":"key-10.20965\/jaciii.2026.p0738-19","doi-asserted-by":"crossref","unstructured":"F. Berns, T. Ramsdorf, and C. Beecks, \u201cMachine learning for storage location prediction in industrial high bay warehouses,\u201d Proc. of ICPR Int. Workshops and Challenges, Part IV, pp. 650-661, 2021. https:\/\/doi.org\/10.1007\/978-3-030-68799-1","DOI":"10.1007\/978-3-030-68799-1_47"},{"key":"key-10.20965\/jaciii.2026.p0738-20","unstructured":"J. Schulman, S. Levine, P. Abbeel et al., \u201cTrust region policy optimization,\u201d Proc. of the 32nd Int. 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Luo, \u201cStacker scheduling and repository location recommendation based on multi-task reinforcement learning,\u201d Computer Engineering, Vol.49, No.2, pp. 279-287,295, 2023."}],"container-title":["Journal of Advanced Computational Intelligence and Intelligent Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.fujipress.jp\/main\/wp-content\/themes\/Fujipress\/hyosetsu.php?ppno=jacii003000030010","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:03:28Z","timestamp":1779203008000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.fujipress.jp\/jaciii\/jc\/jacii003000030738"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,20]]},"references-count":21,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,5,20]]},"published-print":{"date-parts":[[2026,5,20]]}},"URL":"https:\/\/doi.org\/10.20965\/jaciii.2026.p0738","relation":{},"ISSN":["1883-8014","1343-0130"],"issn-type":[{"value":"1883-8014","type":"electronic"},{"value":"1343-0130","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5,20]]}}}