{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T00:36:46Z","timestamp":1705106206541},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684802","type":"print"},{"value":"9781643684819","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,12]]},"abstract":"<jats:p>Aiming at the dynamic flexible scheduling problem in the integrated installation of large-scale laser devices, a deep learning rule acquisition method based on artificial neural network is proposed. Firstly, the typical example is optimized by genetic algorithm, then the task comparison trajectory and characteristic data are extracted from the optimal solution, and the task priority model is generated by deep learning. Finally, the dynamic flexible scheduling decision mode is constructed based on the algorithm model, so as to realize fast response and accurate scheduling in complex, changeable and uncertain production environment. Data experiments and practical cases verify the effectiveness of this method. With the increase of the number of scheduling objects, the computational efficiency of ANN scheduling algorithm is obviously better than GA algorithm in the case of little difference in calculation results.<\/jats:p>","DOI":"10.3233\/faia231229","type":"book-chapter","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T12:56:32Z","timestamp":1705064192000},"source":"Crossref","is-referenced-by-count":0,"title":["Intelligent Assembly Scheduling of Large Laser Devices Based on Neural Network"],"prefix":"10.3233","author":[{"given":"Zhao","family":"Xiong","sequence":"first","affiliation":[{"name":"Laser Fusion Research Center, CAEP, Mianyang 621900, China"}]},{"given":"Chengcheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Laser Fusion Research Center, CAEP, Mianyang 621900, China"}]},{"given":"Dongxia","family":"Hu","sequence":"additional","affiliation":[{"name":"Laser Fusion Research Center, CAEP, Mianyang 621900, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Electronics, Communications and Networks"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA231229","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T12:56:34Z","timestamp":1705064194000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA231229"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,12]]},"ISBN":["9781643684802","9781643684819"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia231229","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,12]]}}}