{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T14:15:42Z","timestamp":1781532942181,"version":"3.54.5"},"reference-count":24,"publisher":"Optica Publishing Group","issue":"4","license":[{"start":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T00:00:00Z","timestamp":1773878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/doi.org\/10.1364\/OA_License_v2#VOR"},{"start":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T00:00:00Z","timestamp":1773878400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/opg.optica.org\/policies\/opg-tdm-policy.json"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Forschung,\n\t\t\t\t\t\tTechnologie und Raumfahrt","doi-asserted-by":"crossref","award":["16KISK250"],"award-info":[{"award-number":["16KISK250"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"crossref"},{"id":"https:\/\/ror.org\/04pz7b180","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Forschung,\n\t\t\t\t\t\tTechnologie und Raumfahrt","doi-asserted-by":"crossref","award":["16KIS2280"],"award-info":[{"award-number":["16KIS2280"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"crossref"},{"id":"https:\/\/ror.org\/04pz7b180","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":["opg.optica.org"],"crossmark-restriction":false},"short-container-title":["J. Opt. Commun. Netw."],"published-print":{"date-parts":[[2026,4,1]]},"abstract":"<jats:p>Multiperiod planning of multilayer transport networks under deep\n\t\t\t\t\ttraffic uncertainties poses significant challenges for network\n\t\t\t\t\toperators. Conventional planning approaches rely on static forecasts\n\t\t\t\t\tand short-term optimizations that fail to capture the long-term\n\t\t\t\t\teconomic trade-offs under highly uncertain demand evolutions. To solve\n\t\t\t\t\tthis problem, this paper applies the concept of flexibility in\n\t\t\t\t\tengineering design to optimize flexible decision rules (FDRs) for\n\t\t\t\t\tadaptive capacity planning. Multiperiod planning is modeled as a\n\t\t\t\t\tpolicy-learning problem, where investment and capacity expansion\n\t\t\t\t\tdecisions are dynamically adjusted based on realized traffic history.\n\t\t\t\t\tThe FDRs are learned using deep reinforcement learning (DRL), enabling\n\t\t\t\t\tthe optimization of long-term economic performance under stochastic\n\t\t\t\t\tdemand scenarios. The approach is assessed for selected OTN-over-WDM\n\t\t\t\t\tnetwork implementation variants using the expected net present value\n\t\t\t\t\t(ENPV) as a performance metric. Compared to conventional planning\n\t\t\t\t\tbaselines, the learned FDRs improve the ENPV by a factor of 9.5% and\n\t\t\t\t\t472.9% in the worst and best cases, respectively.<\/jats:p>","DOI":"10.1364\/jocn.587660","type":"journal-article","created":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T12:00:10Z","timestamp":1771934410000},"page":"338","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Planning multilayer networks under deep uncertainties: an approach based on flexibility in engineering design"],"prefix":"10.1364","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0576-7020","authenticated-orcid":true,"given":"Arantxa","family":"Villavicencio Paz","sequence":"first","affiliation":[{"name":"Adtran Networks SE"},{"name":"Chair of Communication Networks"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7325-1677","authenticated-orcid":true,"given":"Ronald","family":"Romero Reyes","sequence":"additional","affiliation":[{"name":"Chair of Communication Networks"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prashasti","family":"Sahu","sequence":"additional","affiliation":[{"name":"Chair of Communication Networks"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,3,19]]},"reference":[{"key":"jocn-18-4-338-R2","doi-asserted-by":"crossref","DOI":"10.1109\/icc.2011.5962890","article-title":"Impact and handling of demand uncertainty in multiperiod planned networks","volume-title":"IEEE International Conference on Communications (ICC)","author":"Kronberger","year":"2011"},{"key":"jocn-18-4-338-R3","doi-asserted-by":"crossref","DOI":"10.1109\/NETWKS.2010.5624957","article-title":"Multiperiod planning for optical networks","volume-title":"14th International Telecommunications Network Strategy and Planning Symposium (NETWORKS)","author":"Schupke","year":"2010"},{"key":"jocn-18-4-338-R4","doi-asserted-by":"crossref","DOI":"10.1109\/ONDM.2012.6210204","article-title":"Robust upgrade in optical networks under traffic uncertainty","volume-title":"16th International Conference on Optical Network Design and Modelling (ONDM)","author":"Aparicio-Pardo","year":"2012"},{"key":"jocn-18-4-338-R5","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1109\/MCOM.2014.6736760","type":"journal-article","volume":"52","author":"Bauschert","year":"2014","journal-title":"IEEE Commun. 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All rights, including\n\t\t\t\t\tfor text and data mining (TDM), Artificial Intelligence (AI) training,\n\t\t\t\t\tand similar technologies, are reserved.","name":"copyright","label":"Copyright"}]}}