A Many-to-One Algorithm to Solve a Many-to-Many Matching Problem for Routing

  • Wenjing GuoEmail author
  • Wouter Beelaerts van Blokland
  • Rudy R. Negenborn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11184)


This paper investigates the multi-commodity multi-service matching problem of synchromodal hinterland container transportation. To improve the computational efficiency, this paper proposes a many-to-one algorithm to solve the many-to-many matching problem. We assess the performance of the proposed method with 51 instances of the problem, and perform sensitivity analysis to analyze the influence of different demand patterns. The computational results indicate that the algorithm is suitable for large-scale instances of the problem.


Hinterland container transportation Synchromodality Multi-commodity Matching Many-to-one algorithm 



This research is financially supported by the China Scholarship Council under Grant 201606950003.


  1. 1.
    Arslan, A., Agatz, N., Kroon, L.G., Zuidwijk, R.A.: Crowdsourced delivery - a pickup and delivery problem with ad-hoc drivers. SSRN Electron. J. 1–26 (2016)Google Scholar
  2. 2.
    Chen, W., Mes, M., Schutten, M.: Multi-hop driver-parcel matching problem with time windows. Flex. Serv. Manuf. J. 1–37 (2017)Google Scholar
  3. 3.
  4. 4.
    Guo, W., Beelaerts van Blokland, W., Lodewijks, G.: Survey on characteristics and challenges of synchromodal transportation in global cold chains. In: Bektaş, T., Coniglio, S., Martinez-Sykora, A., Voß, S. (eds.) Computational Logistics. LNCS, vol. 10572, pp. 420–434. Springer, Cham (2017). Scholar
  5. 5.
    Guo, W., Beelaerts van Blokland, W., Negenborn, R.R.: Multi-commodity multi-service matching design for hinterland container transportation. Delft University of Technology, Technical report (2018)Google Scholar
  6. 6.
    Mahmoudi, M., Zhou, X.: Finding optimal solutions for vehicle routing problem with pickup and delivery services with time windows: a dynamic programming approach based on state–space–time network representations. Transp. Res. Part B Methodol. 89, 19–42 (2016)CrossRefGoogle Scholar
  7. 7.
    Masoud, N., Jayakrishnan, R.: A real-time algorithm to solve the peer-to-peer ride-matching problem in a flexible ridesharing system. Transp. Res. Part B Methodol. 106, 218–236 (2017)CrossRefGoogle Scholar
  8. 8.
    van Riessen, B., Negenborn, R.R., Dekker, R.: Real-time container transport planning with decision trees based on offline obtained optimal solutions. Decis. Support Syst. 89, 1–16 (2016)CrossRefGoogle Scholar
  9. 9.
    van Riessen, B., Negenborn, R.R., Lodewijks, G., Dekker, R.: Impact and relevance of transit disturbances on planning in intermodal container networks using disturbance cost analysis. Maritime Econ. Logist. 17(4), 440–463 (2014)CrossRefGoogle Scholar
  10. 10.
    SteadieSeifi, M., Dellaert, N., Nuijten, W., Woensel, T.V., Raoufi, R.: Multimodal freight transportation planning: a literature review. Eur. J. Oper. Res. 233(1), 1–15 (2014)CrossRefGoogle Scholar
  11. 11.
    Stiglic, M., Agatz, N., Savelsbergh, M., Gradisar, M.: The benefits of meeting points in ride-sharing systems. Transp. Res. Part B Methodol. 82, 36–53 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Wenjing Guo
    • 1
    Email author
  • Wouter Beelaerts van Blokland
    • 1
  • Rudy R. Negenborn
    • 1
  1. 1.Delft University of TechnologyDelftThe Netherlands

Personalised recommendations