{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T10:57:58Z","timestamp":1764413878319,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030876715"},{"type":"electronic","value":"9783030876722"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-87672-2_38","type":"book-chapter","created":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T21:02:46Z","timestamp":1632258166000},"page":"578-593","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Tackling Uncertainty in Online Multimodal Transportation Planning Using Deep Reinforcement Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2375-5396","authenticated-orcid":false,"given":"Amirreza","family":"Farahani","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8746-8826","authenticated-orcid":false,"given":"Laura","family":"Genga","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4083-0036","authenticated-orcid":false,"given":"Remco","family":"Dijkman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,22]]},"reference":[{"key":"38_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1007\/978-3-030-59747-4_38","volume-title":"Computational Logistics","author":"JC Alves","year":"2020","unstructured":"Alves, J.C., Mateus, G.R.: Deep reinforcement learning and optimization approach for multi-echelon supply chain with uncertain demands. In: Lalla-Ruiz, E., Mes, M., Vo\u00df, S. (eds.) ICCL 2020. LNCS, vol. 12433, pp. 584\u2013599. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59747-4_38"},{"issue":"9","key":"38_CR2","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1016\/0362-546X(89)90096-5","volume":"13","author":"E Barron","year":"1989","unstructured":"Barron, E., Ishii, H.: The Bellman equation for minimizing the maximum cost. Nonlinear Anal. Theory Methods Appl. 13(9), 1067\u20131090 (1989)","journal-title":"Nonlinear Anal. Theory Methods Appl."},{"key":"38_CR3","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1613\/jair.3912","volume":"47","author":"MG Bellemare","year":"2013","unstructured":"Bellemare, M.G., Naddaf, Y., Veness, J., Bowling, M.: The arcade learning environment: an evaluation platform for general agents. J. Artif. Intell. Res. 47, 253\u2013279 (2013)","journal-title":"J. Artif. Intell. Res."},{"key":"38_CR4","doi-asserted-by":"crossref","unstructured":"Bhargavi, K., Babu, B.S.: Soft-set based DDQ scheduler for optimal task scheduling under uncertainty in the cloud. In: 2017 2nd International Conference On Emerging Computation and Information Technologies (ICECIT), pp. 1\u20136. IEEE (2017)","DOI":"10.1109\/ICECIT.2017.8453306"},{"issue":"7","key":"38_CR5","doi-asserted-by":"publisher","first-page":"3980","DOI":"10.3390\/su13073980","volume":"13","author":"T Delbart","year":"2021","unstructured":"Delbart, T., Molenbruch, Y., Braekers, K., Caris, A.: Uncertainty in intermodal and synchromodal transport: Review and future research directions. Sustainability 13(7), 3980 (2021)","journal-title":"Sustainability"},{"issue":"2","key":"38_CR6","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.compind.2012.11.006","volume":"64","author":"A Escudero","year":"2013","unstructured":"Escudero, A., Mu\u00f1uzuri, J., Guadix, J., Arango, C.: Dynamic approach to solve the daily drayage problem with transit time uncertainty. Comput. Ind 64(2), 165\u2013175 (2013)","journal-title":"Comput. Ind"},{"key":"38_CR7","doi-asserted-by":"publisher","first-page":"8557","DOI":"10.1109\/JIOT.2020.3046622","volume":"8","author":"D Fang","year":"2020","unstructured":"Fang, D., Guan, X., Peng, Y., Chen, H., Ohtsuki, T., Han, Z.: Distributed deep reinforcement learning for renewable energy accommodation assessment with communication uncertainty in Internet of Energy. IEEE Internet Things J. 8, 8557\u20138569 (2020)","journal-title":"IEEE Internet Things J."},{"key":"38_CR8","doi-asserted-by":"crossref","unstructured":"Farahani, A., Genga, L., Dijkman, R.: Online multimodal transportation planning using deep reinforcement learning. arXiv preprint arXiv:2105.08374 (2021)","DOI":"10.1109\/SMC52423.2021.9658943"},{"issue":"5","key":"38_CR9","doi-asserted-by":"publisher","first-page":"5775","DOI":"10.1109\/TIA.2020.2986412","volume":"56","author":"Y Gao","year":"2020","unstructured":"Gao, Y., Yang, J., Yang, M., Li, Z.: Deep reinforcement learning based optimal schedule for a battery swapping station considering uncertainties. IEEE Trans. Ind. Appl. 56(5), 5775\u20135784 (2020)","journal-title":"IEEE Trans. Ind. Appl."},{"key":"38_CR10","doi-asserted-by":"publisher","first-page":"102161","DOI":"10.1016\/j.tre.2020.102161","volume":"144","author":"V Gumuskaya","year":"2020","unstructured":"Gumuskaya, V., van Jaarsveld, W., Dijkman, R., Grefen, P., Veenstra, A.: Dynamic barge planning with stochastic container arrivals. Transp. Res. Part E Logist. Transp. Rev. 144, 102161 (2020)","journal-title":"Transp. Res. Part E Logist. Transp. Rev."},{"key":"38_CR11","doi-asserted-by":"crossref","unstructured":"Ma, H., Yu, G., She, Y., Gu, Y., et al.: Waterflooding optimization under geological uncertainties by using deep reinforcement learning algorithms. In: SPE Annual Technical Conference and Exhibition (2019). Society of Petroleum Engineers","DOI":"10.2118\/196190-MS"},{"key":"38_CR12","unstructured":"Mnih, V., et al.: Playing Atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013)"},{"key":"38_CR13","doi-asserted-by":"crossref","unstructured":"Peng, Z., Zhang, Y., Feng, Y., Zhang, T., Wu, Z., Su, H.: Deep reinforcement learning approach for capacitated supply chain optimization under demand uncertainty. In: 2019 Chinese Automation Congress (CAC), pp. 3512\u20133517. IEEE (2019)","DOI":"10.1109\/CAC48633.2019.8997498"},{"key":"38_CR14","first-page":"141","volume":"8","author":"WB Powell","year":"1995","unstructured":"Powell, W.B., Jaillet, P., Odoni, A.: Stochastic and dynamic networks and routing. Handb. Oper. Res. Manag. Sci. 8, 141\u2013295 (1995)","journal-title":"Handb. Oper. Res. Manag. Sci."},{"key":"38_CR15","doi-asserted-by":"crossref","unstructured":"Rivera, A.P., Mes, M.R.: Anticipatory scheduling of freight in a synchromodal transportation network. Transp. Res. Part E Logist. Transp. Rev. 105, 176\u2013194 (2017)","DOI":"10.1016\/j.tre.2016.09.002"},{"key":"38_CR16","unstructured":"Sakib, N.: Highway lane change under uncertainty with deep reinforcement learning based motion planner (2020)"},{"key":"38_CR17","doi-asserted-by":"crossref","unstructured":"Shyalika, C., Silva, T.: Reinforcement learning based an integrated approach for uncertainty scheduling in adaptive environments using MARL. In: 2021 6th International Conference on Inventive Computation Technologies (ICICT), pp. 1204\u20131211. IEEE (2021)","DOI":"10.1109\/ICICT50816.2021.9358727"},{"issue":"1","key":"38_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ejor.2013.06.055","volume":"233","author":"M SteadieSeifi","year":"2014","unstructured":"SteadieSeifi, M., Dellaert, N.P., Nuijten, W., Van Woensel, T., Raoufi, R.: Multimodal freight transportation planning: a literature review. Eur. J. Oper. Res. 233(1), 1\u201315 (2014)","journal-title":"Eur. J. Oper. Res."},{"key":"38_CR19","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/978-3-642-24455-1_33","volume-title":"KI 2011: Advances in Artificial Intelligence","author":"M Tokic","year":"2011","unstructured":"Tokic, M., Palm, G.: Value-difference based exploration: adaptive control between epsilon-greedy and softmax. In: Bach, J., Edelkamp, S. (eds.) KI 2011. LNCS (LNAI), vol. 7006, pp. 335\u2013346. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-24455-1_33"},{"key":"38_CR20","doi-asserted-by":"publisher","unstructured":"Topaloglu, H.: A parallelizable and approximate dynamic programming-based dynamic fleet management model with random travel times and multiple vehicle types. In: Dynamic Fleet Management, pp. 65\u201393. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-0-387-71722-7_4","DOI":"10.1007\/978-0-387-71722-7_4"},{"key":"38_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dss.2016.06.004","volume":"89","author":"B van Riessen","year":"2016","unstructured":"van Riessen, B., Negenborn, R.R., Dekker, R.: Real-time container transport planning with decision trees based on offline obtained optimal solutions. Decis. Supp. Syst. 89, 1\u201316 (2016)","journal-title":"Decis. Supp. Syst."},{"issue":"4","key":"38_CR22","doi-asserted-by":"publisher","first-page":"640","DOI":"10.3390\/rs12040640","volume":"12","author":"K Wan","year":"2020","unstructured":"Wan, K., Gao, X., Hu, Z., Wu, G.: Robust motion control for UAV in dynamic uncertain environments using deep reinforcement learning. Remote Sens. 12(4), 640 (2020)","journal-title":"Remote Sens."},{"key":"38_CR23","doi-asserted-by":"crossref","unstructured":"Wang, P., Li, Y., Shekhar, S., Northrop, W.F.: Uncertainty estimation with distributional reinforcement learning for applications in intelligent transportation systems: a case study. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pp. 3822\u20133827. IEEE (2019)","DOI":"10.1109\/ITSC.2019.8917429"},{"key":"38_CR24","doi-asserted-by":"publisher","first-page":"105928","DOI":"10.1016\/j.ijepes.2020.105928","volume":"119","author":"J Yang","year":"2020","unstructured":"Yang, J., Yang, M., Wang, M., Du, P., Yu, Y.: A deep reinforcement learning method for managing wind farm uncertainties through energy storage system control and external reserve purchasing. Int. J. Electric. Power Energy Syst. 119, 105928 (2020)","journal-title":"Int. J. Electric. Power Energy Syst."},{"key":"38_CR25","unstructured":"Yehia, A.: Understanding uncertainty: a reinforcement learning approach for project-level pavement management systems. PhD thesis, University of British Columbia (2020)"}],"container-title":["Lecture Notes in Computer Science","Computational Logistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87672-2_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T21:55:53Z","timestamp":1673301353000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87672-2_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030876715","9783030876722"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87672-2_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"22 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Logistics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccl22021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccl2021.nl\/","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":"111","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":"42","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":"2.5","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":"2","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)"}}]}}