{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T20:50:32Z","timestamp":1757451032979,"version":"3.37.3"},"reference-count":33,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T00:00:00Z","timestamp":1630886400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71831001"],"award-info":[{"award-number":["71831001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mobile Information Systems"],"published-print":{"date-parts":[[2021,9,6]]},"abstract":"<jats:p>With the intensification of market competition and the development of market globalization, the efficiency of supply chain management orders has become an important part of enterprise competition resources. The competition among enterprises is fierce. To achieve effective customer response quickly, the time for supply chain order management is minimized, and refine the order processing process. This article introduces the strategy research of supply chain management order based on a reinforcement learning algorithm. This article first combines the reinforcement learning algorithm and deep learning algorithm, using the optimal decision-making ability of reinforcement learning algorithm and deep learning algorithm. The combination of data perception and the optimal ability to analyze examine the data of the order process, order cycle, and order delivery process of the supply chain order management and give the optimal decision. The supply chain order management process conducts questionnaire surveys and seminars to understand the current process of supply chain order management and the problems derived from the analysis of data based on the deep learning algorithm. Finally, through the output of the optimal strategy of the reinforcement learning algorithm, the supply chain order management process was improved, and the satisfaction survey was conducted again. The survey showed that the satisfaction was improved, and the satisfaction reached more than 90%.<\/jats:p>","DOI":"10.1155\/2021\/5880795","type":"journal-article","created":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T20:35:24Z","timestamp":1631046924000},"page":"1-11","source":"Crossref","is-referenced-by-count":3,"title":["Application of Reinforcement Learning Algorithm in Delivery Order System under Supply Chain Environment"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3036-2948","authenticated-orcid":true,"given":"Haozhe","family":"Huang","sequence":"first","affiliation":[{"name":"School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China"},{"name":"School of Economics and Management, Guangxi Vocational College of Performing Arts, Nanning 530000, Guangxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4958-971X","authenticated-orcid":true,"given":"Xin","family":"Tan","sequence":"additional","affiliation":[{"name":"Computation Center of Guangxi, Jiaotong Investment Group of Guangxi, Nanning 530000, Guangxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"doi-asserted-by":"publisher","key":"1","DOI":"10.1007\/s12159-016-0132-0"},{"issue":"1","key":"2","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1109\/TASE.2016.2517155","article-title":"Adaptive fault-tolerant tracking control for MIMO discrete-time systems via reinforcement learning algorithm with less learning parameters","volume":"14","author":"L. 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