{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:25:40Z","timestamp":1766067940398,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819722716"},{"type":"electronic","value":"9789819722723"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-2272-3_16","type":"book-chapter","created":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T19:02:10Z","timestamp":1713207730000},"page":"213-227","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dynamic Constrained Robust Optimization over\u00a0Time for\u00a0Operational Indices of\u00a0Pre-oxidation Process"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4472-3908","authenticated-orcid":false,"given":"Yilin","family":"Fang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3806-5348","authenticated-orcid":false,"given":"Ziheng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,16]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Aliniya, Z., Khasteh, S.H.: Dynamic constrained multi-objective optimization with combination response mechanism. Available at SSRN 4123450 (2022)","DOI":"10.2139\/ssrn.4123450"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Azzouz, R., Bechikh, S., Ben\u00a0Said, L.: Multi-objective optimization with dynamic constraints and objectives: new challenges for evolutionary algorithms. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 615\u2013622 (2015)","DOI":"10.1145\/2739480.2754708"},{"key":"16_CR3","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1016\/j.swevo.2017.10.005","volume":"39","author":"R Azzouz","year":"2018","unstructured":"Azzouz, R., Bechikh, S., Said, L.B., Trabelsi, W.: Handling time-varying constraints and objectives in dynamic evolutionary multi-objective optimization. Swarm Evol. Comput. 39, 222\u2013248 (2018)","journal-title":"Swarm Evol. Comput."},{"issue":"11","key":"16_CR4","first-page":"2014","volume":"43","author":"M Chen","year":"2017","unstructured":"Chen, M., Guo, Y., Gong, D., Yang, Z.: A novel dynamic multi-objective robust evolutionary optimization method. Acta Automatica Sinica 43(11), 2014\u20132032 (2017)","journal-title":"Acta Automatica Sinica"},{"issue":"1","key":"16_CR5","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1007\/s40747-022-00824-4","volume":"9","author":"M Chen","year":"2023","unstructured":"Chen, M., Guo, Y., Jin, Y., Yang, S., Gong, D., Yu, Z.: An environment-driven hybrid evolutionary algorithm for dynamic multi-objective optimization problems. Complex Intell. Syst. 9(1), 659\u2013675 (2023)","journal-title":"Complex Intell. Syst."},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Chen, M., Guo, Y., Liu, H., Wang, C., et\u00a0al.: The evolutionary algorithm to find robust pareto-optimal solutions over time. Math. Probl. Eng. 2015 (2015)","DOI":"10.1155\/2015\/814210"},{"issue":"4","key":"16_CR7","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1109\/TEVC.2019.2958075","volume":"24","author":"Q Chen","year":"2019","unstructured":"Chen, Q., Ding, J., Yang, S., Chai, T.: A novel evolutionary algorithm for dynamic constrained multiobjective optimization problems. IEEE Trans. Evol. Comput. 24(4), 792\u2013806 (2019)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"16_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1007\/978-3-540-70928-2_60","volume-title":"Evolutionary Multi-Criterion Optimization","author":"K Deb","year":"2007","unstructured":"Deb, K., Udaya Bhaskara Rao, N., Karthik, S.: Dynamic multi-objective optimization and decision-making using modified NSGA-II: a case study on hydro-thermal power scheduling. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 803\u2013817. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-70928-2_60"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Fang, Y., Zhao, Z., Jin, L., Li, K.: Modified robust optimization over time for process parameter optimization in pre-oxidation process of carbon fiber production. In: 2023 IEEE Congress on Evolutionary Computation (CEC), pp.\u00a01\u20138. IEEE (2023)","DOI":"10.1109\/CEC53210.2023.10254107"},{"key":"16_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1007\/978-3-642-37192-9_62","volume-title":"Applications of Evolutionary Computation","author":"H Fu","year":"2013","unstructured":"Fu, H., Sendhoff, B., Tang, K., Yao, X.: Finding robust solutions to dynamic optimization problems. In: Esparcia-Alc\u00e1zar, A.I. (ed.) EvoApplications 2013. LNCS, vol. 7835, pp. 616\u2013625. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-37192-9_62"},{"issue":"4","key":"16_CR11","first-page":"72","volume":"25","author":"JY Guzm\u00e1n-Gaspar","year":"2020","unstructured":"Guzm\u00e1n-Gaspar, J.Y., Mezura-Montes, E., Dom\u00ednguez-Isidro, S.: Differential evolution in robust optimization over time using a survival time approach. Math. Comput. Appl. 25(4), 72 (2020)","journal-title":"Math. Comput. Appl."},{"key":"16_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105857","volume":"199","author":"Y Huang","year":"2020","unstructured":"Huang, Y., Jin, Y., Hao, K.: Decision-making and multi-objectivization for cost sensitive robust optimization over time. Knowl.-Based Syst. 199, 105857 (2020)","journal-title":"Knowl.-Based Syst."},{"key":"16_CR13","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s12293-012-0090-2","volume":"5","author":"Y Jin","year":"2013","unstructured":"Jin, Y., Tang, K., Yu, X., Sendhoff, B., Yao, X.: A framework for finding robust optimal solutions over time. Memetic Comput. 5, 3\u201318 (2013)","journal-title":"Memetic Comput."},{"key":"16_CR14","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.compositesa.2016.10.018","volume":"91","author":"BA Newcomb","year":"2016","unstructured":"Newcomb, B.A.: Processing, structure, and properties of carbon fibers. Compos. A Appl. Sci. Manuf. 91, 262\u2013282 (2016)","journal-title":"Compos. A Appl. Sci. Manuf."},{"key":"16_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2023.101317","volume":"80","author":"F Wang","year":"2023","unstructured":"Wang, F., Huang, M., Yang, S., Wang, X.: Penalty and prediction methods for dynamic constrained multi-objective optimization. Swarm Evol. Comput. 80, 101317 (2023)","journal-title":"Swarm Evol. Comput."},{"key":"16_CR16","unstructured":"Wei, S.: Dynamic constrained robust evolutionary optimization method. China University of Mining and Technology (2021)"},{"issue":"4","key":"16_CR17","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1109\/TEVC.2021.3060012","volume":"25","author":"D Yazdani","year":"2021","unstructured":"Yazdani, D., Cheng, R., Yazdani, D., Branke, J., Jin, Y., Yao, X.: A survey of evolutionary continuous dynamic optimization over two decades-part B. IEEE Trans. Evol. Comput. 25(4), 630\u2013650 (2021)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Yazdani, D., et al.: Robust optimization over time: a critical review. IEEE Trans. Evol. Comput. (2023)","DOI":"10.1109\/TEVC.2023.3306017"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Yazdani, D., Yazdani, D., Branke, J., Omidvar, M.N., Gandomi, A.H., Yao, X.: Robust optimization over time by estimating robustness of promising regions. IEEE Trans. Evol. Comput. (2022)","DOI":"10.1109\/TEVC.2022.3180590"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Yu, X., Jin, Y., Tang, K., Yao, X.: Robust optimization over time-a new perspective on dynamic optimization problems. In: IEEE Congress on Evolutionary Computation, pp.\u00a01\u20136. IEEE (2010)","DOI":"10.1109\/CEC.2010.5586024"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, X., Fang, Y., Liu, Q., Yazdani, D.: Multi-objective robust optimization over time for dynamic disassembly sequence planning. Int. J. Precis. Eng. Manuf. 1\u201320 (2023)","DOI":"10.1007\/s12541-023-00900-w"}],"container-title":["Communications in Computer and Information Science","Bio-Inspired Computing: Theories and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-2272-3_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T19:11:07Z","timestamp":1713208267000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-2272-3_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819722716","9789819722723"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-2272-3_16","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"16 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BIC-TA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Bio-Inspired Computing: Theories and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bicta2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2023.bicta.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"168","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"64","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}