{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T12:10:02Z","timestamp":1750939802368,"version":"3.41.0"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819615278"},{"type":"electronic","value":"9789819615285"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-1528-5_15","type":"book-chapter","created":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T17:23:56Z","timestamp":1739553836000},"page":"224-237","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Coordinated Multi-regional Logistics Path Planning: A Broad Reinforcement Learning Framework"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9122-6499","authenticated-orcid":false,"given":"Shengwei","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Congcong","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeping","family":"Tong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,15]]},"reference":[{"issue":"4","key":"15_CR1","doi-asserted-by":"publisher","first-page":"1384","DOI":"10.1080\/10556788.2021.2022142","volume":"37","author":"L Di Puglia Pugliese","year":"2022","unstructured":"Di Puglia Pugliese, L., Ferone, D., Festa, P., Guerriero, F., Macrina, G.: Solution approaches for the vehicle routing problem with occasional drivers and time windows. Optim. Methods Softw. 37(4), 1384\u20131414 (2022)","journal-title":"Optim. Methods Softw."},{"issue":"6","key":"15_CR2","doi-asserted-by":"publisher","first-page":"3171","DOI":"10.1109\/TCYB.2019.2955599","volume":"51","author":"L Feng","year":"2019","unstructured":"Feng, L., et al.: Solving generalized vehicle routing problem with occasional drivers via evolutionary multitasking. IEEE Trans. Cybern. 51(6), 3171\u20133184 (2019)","journal-title":"IEEE Trans. Cybern."},{"key":"15_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2021.102994","volume":"123","author":"B Y\u0131ld\u0131z","year":"2021","unstructured":"Y\u0131ld\u0131z, B.: Express package routing problem with occasional couriers. Transport. Res. Part C: Emerg. Technol. 123, 102994 (2021)","journal-title":"Transport. Res. Part C: Emerg. Technol."},{"key":"15_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2019.104806","volume":"113","author":"G Macrina","year":"2020","unstructured":"Macrina, G., Pugliese, L.D.P., Guerriero, F., Laporte, G.: Crowd-shipping with time windows and transshipment nodes. Comput. Oper. Res. 113, 104806 (2020)","journal-title":"Comput. Oper. Res."},{"key":"15_CR5","doi-asserted-by":"crossref","unstructured":"Foerster, J., Farquhar, G., Afouras, T., Nardelli, N., Whiteson, S.: Counterfactual multi-agent policy gradients. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a032 (2018)","DOI":"10.1609\/aaai.v32i1.11794"},{"key":"15_CR6","first-page":"23609","volume":"34","author":"Y Ma","year":"2021","unstructured":"Ma, Y., et al.: A hierarchical reinforcement learning based optimization framework for large-scale dynamic pickup and delivery problems. Adv. Neural. Inf. Process. Syst. 34, 23609\u201323620 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"15_CR7","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.cor.2019.04.023","volume":"109","author":"L Dahle","year":"2019","unstructured":"Dahle, L., Andersson, H., Christiansen, M., Speranza, M.G.: The pickup and delivery problem with time windows and occasional drivers. Comput. Oper. Res. 109, 122\u2013133 (2019)","journal-title":"Comput. Oper. Res."},{"key":"15_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/978-3-030-59747-4_14","volume-title":"Computational Logistics","author":"J Los","year":"2020","unstructured":"Los, J., Schulte, F., Gansterer, M., Hartl, R.F., Spaan, M.T.J., Negenborn, R.R.: Decentralized combinatorial auctions for dynamic and large-scale collaborative vehicle routing. In: Lalla-Ruiz, E., Mes, M., Vo\u00df, S. (eds.) ICCL 2020. LNCS, vol. 12433, pp. 215\u2013230. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59747-4_14"},{"key":"15_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117796","volume":"205","author":"K Lei","year":"2022","unstructured":"Lei, K., et al.: A multi-action deep reinforcement learning framework for flexible job-shop scheduling problem. Expert Syst. Appl. 205, 117796 (2022)","journal-title":"Expert Syst. Appl."},{"key":"15_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106605","volume":"147","author":"FM Defersha","year":"2020","unstructured":"Defersha, F.M., Rooyani, D.: An efficient two-stage genetic algorithm for a flexible job-shop scheduling problem with sequence dependent attached\/detached setup, machine release date and lag-time. Comput. Ind. Eng. 147, 106605 (2020)","journal-title":"Comput. Ind. Eng."},{"issue":"3","key":"15_CR11","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1007\/s11280-022-01026-1","volume":"25","author":"C Zhu","year":"2022","unstructured":"Zhu, C., Ye, D., Zhu, T., Zhou, W.: Time-optimal and privacy preserving route planning for carpool policy. World Wide Web 25(3), 1151\u20131168 (2022)","journal-title":"World Wide Web"},{"key":"15_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.tre.2021.102545","volume":"157","author":"FY Vincent","year":"2022","unstructured":"Vincent, F.Y., Jodiawan, P., Redi, A.P.: Crowd-shipping problem with time windows, transshipment nodes, and delivery options. Transport. Res. Part E: Logist. Transport. Rev. 157, 102545 (2022)","journal-title":"Transport. Res. Part E: Logist. Transport. Rev."},{"key":"15_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111333","volume":"285","author":"C Zhu","year":"2024","unstructured":"Zhu, C., Ye, D., Huo, H., Zhou, W., Zhu, T.: A location-based advising method in teacher-student frameworks. Knowl.-Based Syst. 285, 111333 (2024)","journal-title":"Knowl.-Based Syst."},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Hikima, Y., Akagi, Y., Kim, H., Asami, T.: An improved approximation algorithm for wage determination and online task allocation in crowd-sourcing. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a037, pp. 3977\u20133986 (2023)","DOI":"10.1609\/aaai.v37i4.25512"},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Anari, N., Goel, G., Nikzad, A.: Mechanism design for crowdsourcing: an optimal 1-1\/e competitive budget-feasible mechanism for large markets. In: 2014 IEEE 55th Annual Symposium on Foundations of Computer Science, pp. 266\u2013275. IEEE (2014)","DOI":"10.1109\/FOCS.2014.36"},{"key":"15_CR16","doi-asserted-by":"crossref","unstructured":"Zhu, C., Ye, D., Zhu, T., Zhou, W.: Location-based real-time updated advising method for traffic signal control. IEEE Internet Things J. (2023)","DOI":"10.1109\/JIOT.2023.3342480"},{"key":"15_CR17","unstructured":"Chen, Y., et al.: Can sophisticated dispatching strategy acquired by reinforcement learning?-a case study in dynamic courier dispatching system. arXiv preprint arXiv:1903.02716 (2019)"},{"key":"15_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119118","volume":"214","author":"FY Vincent","year":"2023","unstructured":"Vincent, F.Y., Aloina, G., Jodiawan, P., Gunawan, A., Huang, T.C.: The vehicle routing problem with simultaneous pickup and delivery and occasional drivers. Expert Syst. Appl. 214, 119118 (2023)","journal-title":"Expert Syst. Appl."},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Zhu, C., Cheng, Z., Ye, D., Hussain, F.K., Zhu, T., Zhou, W.: Time-driven and privacy-preserving navigation model for vehicle-to-vehicle communication systems. IEEE Trans. Veh. Technol. (2023)","DOI":"10.1109\/TVT.2023.3248613"},{"key":"15_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2020.105144","volume":"127","author":"C Archetti","year":"2021","unstructured":"Archetti, C., Guerriero, F., Macrina, G.: The online vehicle routing problem with occasional drivers. Comput. Oper. Res. 127, 105144 (2021)","journal-title":"Comput. Oper. Res."},{"key":"15_CR21","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1007\/978-981-97-0834-5_14","volume-title":"Algorithms and Architectures for Parallel Processing","author":"L Shu","year":"2024","unstructured":"Shu, L., et al.: Smart dag task scheduling based on mcts method of multi-strategy learning. In: Tari, Z., Li, K., Wu, H. (eds.) Algorithms and Architectures for Parallel Processing, pp. 224\u2013242. Springer, Singapore (2024). https:\/\/doi.org\/10.1007\/978-981-97-0834-5_14"},{"key":"15_CR22","doi-asserted-by":"publisher","unstructured":"Joe, W., Lau, H.C.: Learning to send reinforcements: coordinating multi-agent dynamic police patrol dispatching and rescheduling via reinforcement learning. In: Elkind, E. (ed.) Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23, pp. 153\u2013161. International Joint Conferences on Artificial Intelligence Organization (2023). https:\/\/doi.org\/10.24963\/ijcai.2023\/18","DOI":"10.24963\/ijcai.2023\/18"},{"issue":"3","key":"15_CR23","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1016\/j.ejor.2021.06.021","volume":"298","author":"X Chen","year":"2022","unstructured":"Chen, X., Ulmer, M.W., Thomas, B.W.: Deep q-learning for same-day delivery with vehicles and drones. Eur. J. Oper. Res. 298(3), 939\u2013952 (2022)","journal-title":"Eur. J. Oper. Res."},{"key":"15_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2024.101576","volume":"87","author":"P Duan","year":"2024","unstructured":"Duan, P., Yu, Z., Gao, K., Meng, L., Han, Y., Ye, F.: Solving the multi-objective path planning problem for mobile robot using an improved NSGA-II algorithm. Swarm Evol. Comput. 87, 101576 (2024)","journal-title":"Swarm Evol. Comput."}],"container-title":["Lecture Notes in Computer Science","Algorithms and Architectures for Parallel Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-1528-5_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T11:29:14Z","timestamp":1750937354000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-1528-5_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819615278","9789819615285"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-1528-5_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"15 February 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICA3PP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Algorithms and Architectures for Parallel Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Macau","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ica3pp2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ica3pp2024.scimeeting.cn\/en\/web\/index\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}