create a website

Optimization and Machine Learning Applied to Last-Mile Logistics: A Review. (2022). Giuffrida, Nadia ; Steudter, Margarete ; Fajardo-Calderin, Jenny ; Werner, Frank ; Pilla, Francesco ; Masegosa, Antonio D.
In: Sustainability.
RePEc:gam:jsusta:v:14:y:2022:i:9:p:5329-:d:804535.

Full description at Econpapers || Download paper

Cited: 3

Citations received by this document

Cites: 83

References cited by this document

Cocites: 20

Documents which have cited the same bibliography

Coauthors: 0

Authors who have wrote about the same topic

Citations

Citations received by this document

  1. The first mile is the hardest: A deep learning-assisted matheuristic for container assignment in first-mile logistics. (2025). Emde, Simon ; Tudoran, Ana Alina.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:324:y:2025:i:1:p:335-350.

    Full description at Econpapers || Download paper

  2. Spatial participatory planning for urban logistics: A GIS-enhanced Real-Time Spatial Delphi approach. (2024). Calleo, Yuri ; Ottomanelli, Michele ; di Zio, Simone ; Pilla, Francesco ; Giuffrida, Nadia.
    In: Research in Transportation Economics.
    RePEc:eee:retrec:v:108:y:2024:i:c:s0739885924000830.

    Full description at Econpapers || Download paper

  3. State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary. (2023). Anwar, Imran ; Mamilla, Rajesh ; Thayyib, P V ; Khan, Mohsin ; Asim, Mohd ; Shamsudheen, M K ; Fatima, Humaira.
    In: Sustainability.
    RePEc:gam:jsusta:v:15:y:2023:i:5:p:4026-:d:1077109.

    Full description at Econpapers || Download paper

References

References cited by this document

  1. Adewumi, A.O.; Adeleke, O.J. A survey of recent advances in vehicle routing problems. Int. J. Syst. Assur. Eng. Manag. 2018, 9, 155–172. [CrossRef]

  2. Albadrani, A.; Alghayadh, F.; Zohdy, M.A.; Aloufi, E.; Olawoyin, R. Performance and Predicting of Inbound Logistics Processes Using Machine Learning. In Proceedings of the 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 27–30 January 2021; pp. 790–795. Sustainability 2022, 14, 5329 14 of 16
    Paper not yet in RePEc: Add citation now
  3. Alcaraz, J.J.; Caballero-Arnaldos, L.; Vales-Alonso, J. Rich vehicle routing problem with last-mile outsourcing decisions. Transp. Res. Part E: Logist. Transp. Rev. 2019, 129, 263–286. [CrossRef]

  4. Andelmin, J.; Bartolini, E. An exact algorithm for the green vehicle routing problem. Transp. Sci. 2017, 51, 1288–1303. [CrossRef]

  5. Available online: http://www.interregeurope.eu/fileadmin/user_upload/plp_uploads/policy_briefs/Sustainable_urban_ logistics.pdf (accessed on 15 March 2022).
    Paper not yet in RePEc: Add citation now
  6. Avci, M.; Topaloglu, S. A hybrid metaheuristic algorithm for heterogeneous vehicle routing problem with simultaneous pickup and delivery. Expert Syst. Appl. 2016, 53, 160–171. [CrossRef]
    Paper not yet in RePEc: Add citation now
  7. Baldacci, R.; Mingozzi, A.; Roberti, R. Recent exact algorithms for solving the vehicle routing problem under capacity and time window constraints. Eur. J. Oper. Res. 2012, 218, 1–6. [CrossRef]

  8. Baller, A.C.; Dabia, S.; Dullaert, W.E.H.; Vigo, D. The vehicle routing problem with partial outsourcing. Transp. Sci. 2020, 54, 1034–1052. [CrossRef]
    Paper not yet in RePEc: Add citation now
  9. Berhan, E.; Beshah, B.; Kitaw, D.; Abraham, A. Stochastic Vehicle Routing Problem: A Literature Survey. J. Inf. Knowl. Manag. 2014, 13, 9848104. [CrossRef]
    Paper not yet in RePEc: Add citation now
  10. Bernardo, M.; Pannek, J. Robust Solution Approach for the Dynamic and Stochastic Vehicle Routing Problem. J. Adv. Transp. 2018, 2018, 9848104. [CrossRef]
    Paper not yet in RePEc: Add citation now
  11. Braekers, K.; Ramaekers, K.; Van Nieuwenhuyse, I. The vehicle routing problem: State of the art classification and review. Comput. Ind. Eng. 2016, 99, 300–313. [CrossRef]
    Paper not yet in RePEc: Add citation now
  12. Bricher, D.; Müller, A. A Supervised Machine Learning Approach for Intelligent Process Automation in Container Logistics. J. Comput. Inf. Sci. Eng. 2020, 20, 031006. [CrossRef]
    Paper not yet in RePEc: Add citation now
  13. Caceres-Cruz, J.; Arias, P.; Guimarans, D.; Riera, D.; Juan, A.A. Rich vehicle routing problem: Survey. ACM Comput. Surv. 2014, 47, 1–28. [CrossRef]
    Paper not yet in RePEc: Add citation now
  14. Caggiani, L.; Colovic, A.; Prencipe, L.P.; Ottomanelli, M. A green logistics solution for last-mile deliveries considering e-vans and e-cargo bikes. Transp. Res. Procedia 2021, 52, 75–82. [CrossRef] Sustainability 2022, 14, 5329 15 of 16
    Paper not yet in RePEc: Add citation now
  15. Calabrò, G.; Le Pira, M.; Giuffrida, N.; Fazio, M.; Inturri, G.; Ignaccolo, M. Modelling the dynamics of fragmented vs. consolidated last-mile e-commerce deliveries via an agent-based model. Transp. Res. Procedia 2022, 62, 155–162. [CrossRef]
    Paper not yet in RePEc: Add citation now
  16. Cattaruzza, D.; Absi, N.; Feillet, D. The multi-trip vehicle routing problem with time windows and release dates. Transp. Sci. 2016, 50, 676–693. [CrossRef]

  17. Cattaruzza, D.; Absi, N.; Feillet, D.; Vidal, T. A memetic algorithm for the Multi Trip Vehicle Routing Problem. Eur. J. Oper. Res. 2014, 236, 833–848. [CrossRef]

  18. Chu, J.C.; Yan, S.; Huang, H.J. A Multi-Trip Split-Delivery Vehicle Routing Problem with Time Windows for Inventory Replenishment Under Stochastic Travel Times. Netw. Spat. Econ. 2017, 17, 41–68. [CrossRef]
    Paper not yet in RePEc: Add citation now
  19. Costa, L.; Contardo, C.; Desaulniers, G. Exact branch-price-and-cut algorithms for vehicle routing. Transp. Sci. 2019, 53, 946–985. [CrossRef]

  20. DHL. Logistics Trend Radar 5th Edition. 2021. Available online: https://www.dhl.com/global-en/home/insights-andinnovation /insights/logistics-trend-radar.html (accessed on 15 March 2022).
    Paper not yet in RePEc: Add citation now
  21. EC. E-Commerce Statistics for Individuals. 2021. Available online: https://ec.europa.eu/eurostat/statistics-explained/index. php/E-commerce_statistics_for_individuals (accessed on 15 March 2022).
    Paper not yet in RePEc: Add citation now
  22. El Ouadi, J.; Errousso, H.; Benhadou, S.; Medromi, H.; Malhene, N. A Machine-Learning Based Approach for Zoning Urban Area in Consolidation Schemes Context. In Proceedings of the 2020 IEEE 13th International Colloquium of Logistics and Supply Chain Management (LOGISTIQUA), Fez, Morocco, 2–4 December 2020; pp. 1–7.
    Paper not yet in RePEc: Add citation now
  23. Elshaer, R.; Awad, H. A taxonomic review of metaheuristic algorithms for solving the vehicle routing problem and its variants. Comput. Ind. Eng. 2020, 140, 106242. [CrossRef]
    Paper not yet in RePEc: Add citation now
  24. Eshtehadi, R.; Demir, E.; Huang, Y. Solving the vehicle routing problem with multi-compartment vehicles for city logistics. Comput. Oper. Res. 2020, 115, 104859. [CrossRef]
    Paper not yet in RePEc: Add citation now
  25. Euchi, J.; Yassine, A.; Chabchoub, H. The dynamic vehicle routing problem: Solution with hybrid metaheuristic approach. Swarm Evol. Comput. 2015, 21, 41–53. [CrossRef]
    Paper not yet in RePEc: Add citation now
  26. Feng, T.; Timmermans, H.J.P. Detecting activity type from GPS traces using spatial and temporal information. Eur. J. Transp. Infrastruct. Res. 2015, 15, 662–674. [CrossRef]
    Paper not yet in RePEc: Add citation now
  27. Ganji, M.; Kazemipoor, H.; Hadji Molana, S.M.; Sajadi, S.M. A green multi-objective integrated scheduling of production and distribution with heterogeneous fleet vehicle routing and time windows. J. Clean. Prod. 2020, 259, 120824. [CrossRef]
    Paper not yet in RePEc: Add citation now
  28. Gao, M.; Feng, Q. Modeling and Forecasting of Urban Logistics Demand Based on Support Vector Machine. In Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining, Moscow, Russia, 23–25 January 2009; pp. 793–796.
    Paper not yet in RePEc: Add citation now
  29. Ghilas, V.; Demir, E.; Van Woensel, T. An adaptive large neighborhood search heuristic for the Pickup and Delivery Problem with Time Windows and Scheduled Lines. Comput. Oper. Res. 2016, 72, 12–30. [CrossRef]
    Paper not yet in RePEc: Add citation now
  30. Goel, R.K.; Bansal, S.R. Hybrid algorithms for rich vehicle routing problems: A survey. In Smart Delivery Systems: Solving Complex Vehicle Routing Problems; Elsevier: Amsterdam, The Netherlands, 2020; pp. 157–184. [CrossRef]
    Paper not yet in RePEc: Add citation now
  31. Golden, B.L.; Raghavan, S.; Wasil, E.A. The Vehicle Routing Problem: Latest Advances and New Challenges; Springer: New York, NY, USA, 2008.
    Paper not yet in RePEc: Add citation now
  32. Grabenschweiger, J.; Doerner, K.F.; Hartl, R.F.; Savelsbergh, M.W.P. The vehicle routing problem with heterogeneous locker boxes. Cent. Eur. J. Oper. Res. 2021, 29, 113–142. [CrossRef]
    Paper not yet in RePEc: Add citation now
  33. Grangier, P.; Gendreau, M.; Lehuédé, F.; Rousseau, L.M. An adaptive large neighborhood search for the two-echelon multiple-trip vehicle routing problem with satellite synchronization. Eur. J. Oper. Res. 2016, 254, 80–91. [CrossRef]

  34. Gu, W.; Cattaruzza, D.; Ogier, M.; Semet, F. Adaptive large neighborhood search for the commodity constrained split delivery VRP. Comput. Oper. Res. 2019, 112, 104761. [CrossRef]
    Paper not yet in RePEc: Add citation now
  35. Guermazi, Y.; Sellami, S.; Boucelma, O. Address Validation in Transportation and Logistics: A Machine Learning Based Entity Matching Approach. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Ghent, Belgium, 14–18 September 2020; Springer: Cham, Switzerland, 2020; pp. 320–334.
    Paper not yet in RePEc: Add citation now
  36. Gutierrez-Rodríguez, A.E.; Conant-Pablos, S.E.; Ortiz-Bayliss, J.C.; Terashima-Marín, H. Selecting meta-heuristics for solving vehicle routing problems with time windows via meta-learning. Expert Syst. Appl. 2019, 118, 470–481. [CrossRef]
    Paper not yet in RePEc: Add citation now
  37. Hassanzadeh, A.; Rasti-Barzoki, M. Minimizing total resource consumption and total tardiness penalty in a resource allocation supply chain scheduling and vehicle routing problem. Appl. Soft Comput. J. 2017, 58, 307–323. [CrossRef]
    Paper not yet in RePEc: Add citation now
  38. Hess, A.; Spinler, S.; Winkenbach, M. Real-time demand forecasting for an urban delivery platform. Transp. Res. Part E Logist. Transp. Rev. 2021, 145, 102147. [CrossRef]

  39. Hosseinabadi, A.A.; Slowik, A.; Sadeghilalimi, M.; Farokhzad, M.; Babazadeh Shareh, M.; Sangaiah, A.K. An Ameliorative Hybrid Algorithm for Solving the Capacitated Vehicle Routing Problem. IEEE Access 2019, 7, 175454–175465. [CrossRef]
    Paper not yet in RePEc: Add citation now
  40. Jabir, E.; Panicker, V.V.; Sridharan, R. Design and development of a hybrid ant colony-variable neighbourhood search algorithm for a multi-depot green vehicle routing problem. Transp. Res. Part D Transp. Environ. 2017, 57, 422–457. [CrossRef] Sustainability 2022, 14, 5329 16 of 16
    Paper not yet in RePEc: Add citation now
  41. Jozefowiez, N.; Semet, F.; Talbi, E.G. Multi-objective vehicle routing problems. Eur. J. Oper. Res. 2008, 189, 293–309. [CrossRef]
    Paper not yet in RePEc: Add citation now
  42. Kheirkhahzadeh, M.; Barforoush, A.A. A hybrid algorithm for the vehicle routing problem. In Proceedings of the 2009 IEEE Congress on Evolutionary Computation, CEC 2009, Trondheim, Norway, 18–21 May 2009; pp. 1791–1798. [CrossRef]
    Paper not yet in RePEc: Add citation now
  43. Kiba-Janiak, M.; Marcinkowski, J.; Jagoda, A.; Skowrońska, A. Sustainable last mile delivery on e-commerce market in cities from the perspective of various stakeholders. Literature review. Sustain. Cities Soc. 2021, 71, 102984. [CrossRef]
    Paper not yet in RePEc: Add citation now
  44. Knoll, D.; Prüglmeier, M.; Reinhart, G. Predicting future inbound logistics processes using machine learning. Procedia CIRP 2016, 52, 145–150. [CrossRef]
    Paper not yet in RePEc: Add citation now
  45. Kretzschmar, J.; Gebhardt, K.; Theiß, C.; Schau, V. Range prediction models for e-vehicles in urban freight logistics based on machine learning. In International Conference on Data Mining and Big Data, Bali, Indonesia, 25–30 June 2016; Springer: Cham, Switzerland, 2016; pp. 175–184.
    Paper not yet in RePEc: Add citation now
  46. Kumar, S.V.; Thansekhar, M.R.; Saravanan, R.; Amali, M.J.S. Demonstrating the importance of using total time balance instead of route balance on a multi-objective vehicle routing problem with time windows. Int. J. Adv. Manuf. Technol. 2018, 98, 1287–1306. [CrossRef]
    Paper not yet in RePEc: Add citation now
  47. Kumar, S.V.; Thansekhar, M.R.; Saravanan, R.; Amali, M.J.S. Solving multi-objective vehicle routing problem with time windows by FAGA. Procedia Eng. 2014, 97, 2176–2185. [CrossRef]
    Paper not yet in RePEc: Add citation now
  48. Lagos, C.; Guerrero, G.; Cabrera, E.; Moltedo-Perfetti, A.S.; Johnson, F.; Paredes, F. An improved particle swarm optimization algorithm for the VRP with simultaneous pickup and delivery and time windows. IEEE Lat. Am. Trans. 2018, 16, 1732–1740. [CrossRef]
    Paper not yet in RePEc: Add citation now
  49. Lahyani, R.; Khemakhem, M.; Semet, F. Rich vehicle routing problems: From a taxonomy to a definition. Eur. J. Oper. Res. 2015, 241, 1–14. [CrossRef]

  50. Lickert, H.; Wewer, A.; Dittmann, S.; Bilge, P.; Dietrich, F. Selection of Suitable Machine Learning Algorithms for Classification Tasks in Reverse Logistics. Procedia CIRP 2021, 96, 272–277. [CrossRef]
    Paper not yet in RePEc: Add citation now
  51. Lin, N.; Shi, Y.; Zhang, T.; Wang, X. An Effective Order-Aware Hybrid Genetic Algorithm for Capacitated Vehicle Routing Problems in Internet of Things. IEEE Access 2019, 7, 86102–86114. [CrossRef]
    Paper not yet in RePEc: Add citation now
  52. Lin, S.W.; Lee, Z.J.; Ying, K.C.; Lee, C.Y. Applying hybrid meta-heuristics for capacitated vehicle routing problem. Expert Syst. Appl. 2009, 36, 1505–1512. [CrossRef]
    Paper not yet in RePEc: Add citation now
  53. Liu, C.; Zhang, Z.; Su, X.; Qin, H. A hybrid algorithm for the vehicle routing problem with compatibility constraints. In Proceedings of the 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), Zhuhai, China, 27–29 March 2018; pp. 1–6. [CrossRef]
    Paper not yet in RePEc: Add citation now
  54. Liu, K.; Li, N.; Kolmanovsky, I.; Girard, A. A vehicle routing problem with dynamic demands and restricted failures solved using stochastic predictive control. In Proceedings of the 2019 American Control Conference (ACC), Philadelphia, PA, USA, 10–12 July 2019; pp. 1885–1890. [CrossRef]
    Paper not yet in RePEc: Add citation now
  55. Liu, S.C.; Lu, M.C.; Chung, C.H. A hybrid heuristic method for the periodic inventory routing problem. Int. J. Adv. Manuf. Technol. 2016, 85, 2345–2352. [CrossRef]
    Paper not yet in RePEc: Add citation now
  56. Marcucci, E.; Gatta, V.; Le Pira, M.; Hansson, L.; Bråthen, S. Digital Twins: A Critical Discussion on Their Potential for Supporting Policy-Making and Planning in Urban Logistics. Sustainability 2020, 12, 10623. [CrossRef]

  57. Mavrovouniotis, M.; Yang, S. Ant algorithms with immigrants schemes for the dynamic vehicle routing problem. Inf. Sci. 2015, 294, 456–477. [CrossRef]
    Paper not yet in RePEc: Add citation now
  58. Mor, A.; Speranza, M.G. Vehicle routing problems over time: A survey. 4OR 2020, 18, 129–149. [CrossRef]

  59. Okulewicz, M.; Mańdziuk, J. A metaheuristic approach to solve Dynamic Vehicle Routing Problem in continuous search space. Swarm Evol. Comput. 2019, 48, 44–61. [CrossRef]
    Paper not yet in RePEc: Add citation now
  60. Orenstein, I.; Raviv, T.; Sadan, E. Flexible parcel delivery to automated parcel lockers: Models, solution methods and analysis. EURO J. Transp. Logist. 2019, 8, 683–711. [CrossRef]

  61. Oyola, J.; Arntzen, H.; Woodruff, D.L. The stochastic vehicle routing problem, a literature review, part I: Models. EURO J. Transp. Logist. 2018, 7, 193–221. [CrossRef]

  62. Oyola, J.; Arntzen, H.; Woodruff, D.L. The stochastic vehicle routing problem, a literature review, Part II: Solution methods. EURO J. Transp. Logist. 2017, 6, 349–388. [CrossRef]

  63. Perboli, G.; Rosano, M.; Saint-Guillain, M.; Rizzo, P. Simulation–optimisation framework for City Logistics: An application on multimodal last-mile delivery. IET Intell. Transp. Syst. 2018, 12, 262–269. [CrossRef]
    Paper not yet in RePEc: Add citation now
  64. Pillac, V.; Gendreau, M.; Guéret, C.; Medaglia, A.L. A review of dynamic vehicle routing problems. Eur. J. Oper. Res. 2013, 225, 1–11. [CrossRef]

  65. Psaraftis, H.N.; Wen, M.; Kontovas, C.A. Dynamic vehicle routing problems: Three decades and counting. Networks 2016, 67, 3–31. [CrossRef]
    Paper not yet in RePEc: Add citation now
  66. Qin, G.; Tao, F.; Li, L. A vehicle routing optimization problem for cold chain logistics considering customer satisfaction and carbon emissions. Int. J. Environ. Res. Public Health 2019, 16, 576. [CrossRef]
    Paper not yet in RePEc: Add citation now
  67. Rosen, O.; Medvedev, A. An on-line algorithm for anomaly detection in trajectory data. In Proceedings of the 2012 American Control Conference (ACC), Montreal, QC, Canada, 27–29 June 2012; pp. 1117–1122.
    Paper not yet in RePEc: Add citation now
  68. Sarikan, S.S.; Ozbayoglu, A.M. Anomaly detection in vehicle traffic with image processing and machine learning. Procedia Comput. Sci. 2018, 140, 64–69. [CrossRef]
    Paper not yet in RePEc: Add citation now
  69. Savic, M.; Lukic, M.; Danilovic, D.; Bodroski, Z.; Bajovic, D.; Mezei, I.; Vukobratovic, D.; Skrbic, S.; Jakovetic, D. Deep Learning Anomaly Detection for Cellular IoT with Applications in Smart Logistics. IEEE Access 2021, 9, 59406–59419. [CrossRef]
    Paper not yet in RePEc: Add citation now
  70. Simeonova, L.; Wassan, N.; Salhi, S.; Nagy, G. The heterogeneous fleet vehicle routing problem with light loads and overtime: Formulation and population variable neighbourhood search with adaptive memory. Expert Syst. Appl. 2018, 114, 183–195. [CrossRef]
    Paper not yet in RePEc: Add citation now
  71. Sindhwani, V.; Sidahmed, H.; Choromanski, K.; Jones, B. Unsupervised Anomaly Detection for Self-flying Delivery Drones. In Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 31 May–31 August 2020; pp. 186–192.
    Paper not yet in RePEc: Add citation now
  72. Sumalee, A.; Uchida, K.; Lam, W.H.K. Stochastic multi-modal transport network under demand uncertainties and adverse weather condition. Transp. Res. Part C: Emerg. Technol. 2011, 19, 338–350. [CrossRef]
    Paper not yet in RePEc: Add citation now
  73. Tamayo, S.; Combes, F.; Gaudron, A. Unsupervised machine learning to analyze City Logistics through Twitter. Transp. Res. Procedia 2020, 46, 220–228. [CrossRef]
    Paper not yet in RePEc: Add citation now
  74. Tian, Z.; Zhong, R.Y.; Vatankhah Barenji, A.; Wang, Y.T.; Li, Z.; Rong, Y. A blockchain-based evaluation approach for customer delivery satisfaction in sustainable urban logistics. Int. J. Prod. Res. 2021, 59, 2229–2249. [CrossRef]

  75. Van Eck, N.J.; Waltman, L. Text mining and visualization using VOSviewer. ISSI Newsl. 2011, 7, 50–54.
    Paper not yet in RePEc: Add citation now
  76. Vidal, T.; Battarra, M.; Subramanian, A.; Erdogan, G. Hybrid metaheuristics for the Clustered Vehicle Routing Problem. Comput. Oper. Res. 2015, 58, 87–99. [CrossRef]
    Paper not yet in RePEc: Add citation now
  77. Wang, J.; Ren, W.; Zhang, Z.; Huang, H.; Zhou, Y. A Hybrid Multiobjective Memetic Algorithm for Multiobjective Periodic Vehicle Routing Problem with Time Windows. IEEE Trans. Syst. Man Cybern. Syst. 2020, 50, 4732–4745. [CrossRef]
    Paper not yet in RePEc: Add citation now
  78. Wang, J.; Zhou, Y.; Wang, Y.; Zhang, J.; Chen CL, P.; Zheng, Z. Multiobjective Vehicle Routing Problems with Simultaneous Delivery and Pickup and Time Windows: Formulation, Instances, and Algorithms. IEEE Trans. Cybern. 2016, 46, 582–594. [CrossRef]
    Paper not yet in RePEc: Add citation now
  79. Wojtusiak, J.; Warden, T.; Herzog, O. Machine learning in agent-based stochastic simulation: Inferential theory and evaluation in transportation logistics. Comput. Math. Appl. 2012, 64, 3658–3665. [CrossRef]
    Paper not yet in RePEc: Add citation now
  80. Yan, X.; Xiao, B.; Xiao, Y.; Zhao, Z.; Ma, L.; Wang, N. Skill Vehicle Routing Problem with Time Windows Considering Dynamic Service Times and Time-Skill-Dependent Costs. IEEE Access 2019, 7, 77208–77221. [CrossRef]
    Paper not yet in RePEc: Add citation now
  81. Zhang, H.; Zhang, Q.; Ma, L.; Zhang, Z.; Liu, Y. A hybrid ant colony optimization algorithm for a multi-objective vehicle routing problem with flexible time windows. Inf. Sci. 2019, 490, 166–190. [CrossRef]
    Paper not yet in RePEc: Add citation now
  82. Zhao, M.; Ji, S.; Wei, Z. Risk prediction and risk factor analysis of urban logistics to public security based on PSO-GRNN algorithm. PLoS ONE 2020, 15, e0238443. [CrossRef] [PubMed]

  83. Zhao, S.; Sheng, Y.; Dong, Y.; Chang, E.I.; Xu, Y. Maskflownet: Asymmetric feature matching with learnable occlusion mask. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 13–19 June 2020; pp. 6278–6287.
    Paper not yet in RePEc: Add citation now

Cocites

Documents in RePEc which have cited the same bibliography

  1. Mobile COVID-19 vaccination scheduling with capacity selection. (2025). Wang, Zheng ; Coelho, Leandro C ; Tang, Lianhua ; Li, Yantong ; Zhang, Shuai.
    In: Transportation Research Part E: Logistics and Transportation Review.
    RePEc:eee:transe:v:193:y:2025:i:c:s1366554524004174.

    Full description at Econpapers || Download paper

  2. Optimal Transportation Planning for a Do-It-Yourself Retailer with a Zone-Based Tariff. (2024). Tuma, Niklas ; Ostermeier, Manuel ; Hubner, Alexander.
    In: Interfaces.
    RePEc:inm:orinte:v:54:y:2024:i:4:p:312-328.

    Full description at Econpapers || Download paper

  3. How managerial perspectives affect the optimal fleet size and mix model: a multi-objective approach. (2023). Sarangi, Sudipta ; Sabounchi, Nasim S.
    In: OPSEARCH.
    RePEc:spr:opsear:v:60:y:2023:i:1:d:10.1007_s12597-022-00603-2.

    Full description at Econpapers || Download paper

  4. An Improved Genetic Algorithm for the Granularity-Based Split Vehicle Routing Problem with Simultaneous Delivery and Pickup. (2023). Qin, Zihang ; Liu, Yuxin.
    In: Mathematics.
    RePEc:gam:jmathe:v:11:y:2023:i:15:p:3328-:d:1205652.

    Full description at Econpapers || Download paper

  5. Adaptive Large Neighborhood Search Metaheuristic for the Capacitated Vehicle Routing Problem with Parcel Lockers. (2023). Ali, Islam ; Eltawil, Amr ; Saker, Amira.
    In: Logistics.
    RePEc:gam:jlogis:v:7:y:2023:i:4:p:72-:d:1256124.

    Full description at Econpapers || Download paper

  6. An exact algorithm for the pickup and delivery problem with crowdsourced bids and transshipment. (2023). Pan, Kai ; Li, Jiliu ; Qin, HU ; Su, E.
    In: Transportation Research Part B: Methodological.
    RePEc:eee:transb:v:177:y:2023:i:c:s019126152300156x.

    Full description at Econpapers || Download paper

  7. Two meta-heuristics for solving the capacitated vehicle routing problem: the case of the Tunisian Post Office. (2022). Krichen, Saoussen ; Limam, Olfa ; Sbai, Ines.
    In: Operational Research.
    RePEc:spr:operea:v:22:y:2022:i:1:d:10.1007_s12351-019-00543-8.

    Full description at Econpapers || Download paper

  8. Using state-space shortest-path heuristics to solve the long-haul point-to-point vehicle routing and driver scheduling problem subject to hours-of-service regulatory constraints. (2022). Mayerle, Sergio Fernando ; Figueiredo, Joo Neiva ; Genaro, Daiane Maria.
    In: Journal of Heuristics.
    RePEc:spr:joheur:v:28:y:2022:i:1:d:10.1007_s10732-021-09489-7.

    Full description at Econpapers || Download paper

  9. Routing protocol based ant colony optimization system for hybrid sensor and vehicular networks. (2022). Sadou, Malika ; Bouallouche-Medjkoune, Louiza.
    In: International Journal of System Assurance Engineering and Management.
    RePEc:spr:ijsaem:v:13:y:2022:i:6:d:10.1007_s13198-022-01751-w.

    Full description at Econpapers || Download paper

  10. Optimization and Machine Learning Applied to Last-Mile Logistics: A Review. (2022). Giuffrida, Nadia ; Steudter, Margarete ; Fajardo-Calderin, Jenny ; Werner, Frank ; Pilla, Francesco ; Masegosa, Antonio D.
    In: Sustainability.
    RePEc:gam:jsusta:v:14:y:2022:i:9:p:5329-:d:804535.

    Full description at Econpapers || Download paper

  11. Public Acceptance of Last-Mile Shuttle Bus Services with Automation and Electrification in Cold-Climate Environments. (2022). Pei, Yulong ; Wang, Naihui ; Fu, Hao.
    In: Sustainability.
    RePEc:gam:jsusta:v:14:y:2022:i:21:p:14383-:d:961846.

    Full description at Econpapers || Download paper

  12. Online model-based reinforcement learning for decision-making in long distance routes. (2022). Caballero-Arnaldos, Luis ; Alcaraz, Juan J ; Losilla, Fernando.
    In: Transportation Research Part E: Logistics and Transportation Review.
    RePEc:eee:transe:v:164:y:2022:i:c:s136655452200179x.

    Full description at Econpapers || Download paper

  13. The ground handler dock capacitated pickup and delivery problem with time windows: A collaborative framework for air cargo operations. (2022). Fazi, Stefano ; Bombelli, Alessandro.
    In: Transportation Research Part E: Logistics and Transportation Review.
    RePEc:eee:transe:v:159:y:2022:i:c:s1366554522000011.

    Full description at Econpapers || Download paper

  14. Network Mode Optimization for the DHL Supply Chain. (2021). Dang, Yibo ; Allen, Theodore T ; Singh, Manjeet.
    In: Interfaces.
    RePEc:inm:orinte:v:51:y:2021:i:3:p:179-199.

    Full description at Econpapers || Download paper

  15. A survey of finished vehicle distribution and related problems from an optimization perspective. (2021). Chen, Zhi-Long ; Sun, Yanshuo ; Kirtonia, Sajeeb.
    In: Transportation Research Part E: Logistics and Transportation Review.
    RePEc:eee:transe:v:149:y:2021:i:c:s1366554521000764.

    Full description at Econpapers || Download paper

  16. Real-time demand forecasting for an urban delivery platform. (2021). Spinler, Stefan ; Winkenbach, Matthias ; Hess, Alexander.
    In: Transportation Research Part E: Logistics and Transportation Review.
    RePEc:eee:transe:v:145:y:2021:i:c:s1366554520307936.

    Full description at Econpapers || Download paper

  17. A unified model framework for the multi-attribute consistent periodic vehicle routing problem. (2020). Baldoquin, Maria Gulnara ; Diaz-Ramirez, Jenny ; Martinez, Jairo A.
    In: PLOS ONE.
    RePEc:plo:pone00:0237014.

    Full description at Econpapers || Download paper

  18. A cooperative rich vehicle routing problem in the last-mile logistics industry in rural areas. (2020). Ma, Zu-Jun ; Dai, Ying ; Yang, Fei.
    In: Transportation Research Part E: Logistics and Transportation Review.
    RePEc:eee:transe:v:141:y:2020:i:c:s136655452030675x.

    Full description at Econpapers || Download paper

  19. The long-haul full-load vehicle routing and truck driver scheduling problem with intermediate stops: An economic impact evaluation of Brazilian policy. (2020). Mayerle, Sergio Fernando ; de Figueiredo, Joo Neiva ; Rodrigues, Hidelbrando Ferreira ; de Genaro, Daiane Maria.
    In: Transportation Research Part A: Policy and Practice.
    RePEc:eee:transa:v:140:y:2020:i:c:p:36-51.

    Full description at Econpapers || Download paper

  20. Cooperative game-theoretic features of cost sharing in location-routing. (2018). Osička, Ondřej ; Guajardo, Mario ; van Oost, Thibault.
    In: Discussion Papers.
    RePEc:hhs:nhhfms:2018_011.

    Full description at Econpapers || Download paper

Coauthors

Authors registered in RePEc who have wrote about the same topic

Report date: 2025-09-24 09:29:42 || Missing content? Let us know

CitEc is a RePEc service, providing citation data for Economics since 2001. Last updated August, 3 2024. Contact: Jose Manuel Barrueco.