{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:49:19Z","timestamp":1701478159604},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684444","type":"print"},{"value":"9781643684451","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"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":[[2023,11,30]]},"abstract":"<jats:p>Effective integration of process planning and shop scheduling is the main way to solve the problems of low equipment utilization and poor production efficiency when process planning and shop scheduling are optimized separately. In order to achieve green and low-carbon manufacturing, this paper firstly takes the minimization of total carbon emissions, the maximum time of completion and delay time in the manufacturing process as the optimization goals. On account of the principle of nonlinear process planning, a multi-objective integrated process planning is established. Then, the NSGA-II genetic algorithm is used to solve the problem, and the Pareto optimal solution set is obtained by optimization; and the TOPSIS decision method based on the entropy weight method is proposed to select the optimal process route and scheduling scheme from the Pareto optimal solution set. Finally, combined with cases, the validity of the model and the solution process is verified and also provides a basis for the decision of scheduling scheme in the actual production process.<\/jats:p>","DOI":"10.3233\/faia230884","type":"book-chapter","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:56:32Z","timestamp":1701446192000},"source":"Crossref","is-referenced-by-count":0,"title":["Solution and Decision of Multi-Objective Integrated Process Planning and Scheduling Problem"],"prefix":"10.3233","author":[{"given":"Li","family":"Ou","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruixue","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingbin","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Advances in Artificial Intelligence, Big Data and Algorithms"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230884","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:56:37Z","timestamp":1701446197000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230884"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"ISBN":["9781643684444","9781643684451"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230884","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,30]]}}}