{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,2,11]],"date-time":"2024-02-11T12:33:50Z","timestamp":1707654830563},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684888","type":"print"},{"value":"9781643684895","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T00:00:00Z","timestamp":1707264000000},"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":[[2024,2,7]]},"abstract":"<jats:p>In order to reduce therework iteration in the product development process, the optimization analysis of product design process based on reinforcement learning (multi-objective process optimization genetic algorithm based on design structure matrix (DSM) theory) is proposed. By optimizing the task execution sequence, therework in the product development process can be reduced to compress the progress and reduce the cost. The optimization algorithm is an improved genetic (GA) algorithm, in which time and cost are considered in the fitness function. In the selection, crossover and mutation operators, the strategy of maintaining optimal solution is adopted. The simulation results show that the optimization algorithm can reduce the development time by 30% \u223c 40% and the cost by 7% \u223c 20% for product development projects with high task coupling. Conclusion: The optimization algorithm can effectively reduce therework iteration in the project development process, thus shortening the product development time and saving the development cost.<\/jats:p>","DOI":"10.3233\/faia231449","type":"book-chapter","created":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T14:29:00Z","timestamp":1707488940000},"source":"Crossref","is-referenced-by-count":0,"title":["Optimization Analysis of Product Design Process Based on Reinforcement Learning Algorithm"],"prefix":"10.3233","author":[{"given":"Yuping","family":"Hu","sequence":"first","affiliation":[{"name":"Academy of art and design, Shaoyang University, Shaoyang 422099, Hunan, China"}]},{"given":"Qian","family":"Li","sequence":"additional","affiliation":[{"name":"School of Jewelry and Art Design, Wuzhou University, Wuzhou 543003, Guangxi, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Design Studies and Intelligence Engineering"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA231449","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T14:29:01Z","timestamp":1707488941000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA231449"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,7]]},"ISBN":["9781643684888","9781643684895"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia231449","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,7]]}}}