Skip to main content
Log in

Location-routing and cost-sharing models under joint distribution

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

This article introduces the concept of joint distribution context into the location-routing problem (LRP) to tackle the issue of resource waste and high costs in distribution centers (DCs) for multiple companies. Fixed costs of constructing DCs, fixed costs of hiring vehicles, and routing costs are taken into account when constructing the mixed integer programming (MIP) model. A two-stage algorithm is designed to solve this problem. In the first stage, K-means clustering is used to group demand nodes with vehicle capacity constraints. In the second stage, the simplified LRP model is solved by Lingo, and locations of DCs and routing schemes are obtained. Additionally, a cost-sharing model based on the desirability function is developed to address the cost allocation problem among companies. The results and sensitivity analysis demonstrate that the joint distribution can effectively reduce costs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

Enquiries about data availability should be directed to the authors.

References

  1. Botsman, R.: Defining the sharing economy: what is collaborative consumption–and what isn’t. Fast Company 27(1), 2015 (2015)

    Google Scholar 

  2. Zhang, C., Chen, J., Raghunathan, S.: Two-Sided Platform Competition in a Sharing Economy. Manage. Sci. 68(12), 8909–8932 (2022). https://doi.org/10.1287/mnsc.2022.4302

    Article  Google Scholar 

  3. Liu, G.K., Hu, J.Y., Yang, Y., Xia, S.M., Lim, M.K.: Vehicle routing problem in cold Chain logistics: A joint distribution model with carbon trading mechanisms. Resour. Conserv. and Recycl. 156, 104715 (2020). https://doi.org/10.1016/j.resconrec.2020.104715

    Article  Google Scholar 

  4. Ren, X.Y., Jiang, X.X., Ren, L.Y., Meng, L.: A multi-center joint distribution optimization model considering carbon emissions and customer satisfaction. Math. Biosci. Eng. 20(1), 683–706 (2023). https://doi.org/10.3934/mbe.2023031

    Article  Google Scholar 

  5. Wang, Y., Ma, X.L., Liu, M.W., Gong, K., Liu, Y., Xu, M.Z., Wang, Y.H.: Cooperation and profit allocation in two-echelon logistics joint distribution network optimization. Appl. Soft Comput. 56, 143–157 (2017). https://doi.org/10.1016/j.asoc.2017.02.025

    Article  Google Scholar 

  6. Prins, C.: A simple and effective evolutionary algorithm for the vehicle routing problem. Comput. Oper. Res. 31(12), 1985–2002 (2004). https://doi.org/10.1016/s0305-0548(03)00158-8

    Article  MathSciNet  Google Scholar 

  7. Salhi, S., Imran, A., Wassan, N.A.: The multi-depot vehicle routing problem with heterogeneous vehicle fleet: Formulation and a variable neighborhood search implementation. Comput. Oper. Res. 52, 315–325 (2014). https://doi.org/10.1016/j.cor.2013.05.011

    Article  MathSciNet  Google Scholar 

  8. Derbel, H., Jarboui, B., Hanafi, S., Chabchoub, H.: Genetic algorithm with iterated local search for solving a location-routing problem. Expert Syst. Appl. 39(3), 2865–2871 (2012). https://doi.org/10.1016/j.eswa.2011.08.146

    Article  Google Scholar 

  9. Prins, C., Prodhon, C., Calvo, R.W.: Solving the capacitated location-routing problem by a GRASP complemented by a learning process and a path relinking. 4OR 4(3), 221–238 (2006). https://doi.org/10.1007/s10288-006-0001-9

    Article  MathSciNet  Google Scholar 

  10. Barletta, C., Garn, W., Turner, C., Fallah, S.: Hybrid fleet capacitated vehicle routing problem with flexible Monte-Carlo Tree search. Int. J Sys. Sci. Oper. Logist (2023). https://doi.org/10.1080/23302674.2022.2102265

    Article  Google Scholar 

  11. Beasley, J.E.: Route 1st - cluster 2nd methods for vehicle-routing. Omega-Int. J. Manage. Sci. 11(4), 403–408 (1983). https://doi.org/10.1016/0305-0483(83)90033-6

    Article  Google Scholar 

  12. Miranda-Bront, J.J., Curcio, B., Mendez-Diaz, I., Montero, A., Pousa, F., Zabala, P.: A cluster-first route-second approach for the swap body vehicle routing problem. Ann. Oper. Res. 253(2), 935–956 (2017). https://doi.org/10.1007/s10479-016-2233-1

    Article  MathSciNet  Google Scholar 

  13. Villalba, A.F.L., La Rotta, E.C.G.: Clustering and heuristics algorithm for the vehicle routing problem with time windows. Int. J. Ind. Eng. Comput. 13(2), 165–184 (2022). https://doi.org/10.5267/j.ijiec.2021.12.002

    Article  Google Scholar 

  14. Barreto, S., Ferreira, C., Paixao, J., Santos, B.S.: Using clustering analysis location-routing in a capacitated problem. Eur. J. Oper. Res. 179(3), 968–977 (2007). https://doi.org/10.1016/j.ejor.2005.06.074

    Article  Google Scholar 

  15. Rui Borges, L., Sérgio, B., Carlos, F., Beatriz Sousa, S.: A decision-support tool for a capacitated location-routing problem. Decision Support Sys. 46(1), 366–375 (2008). https://doi.org/10.1016/j.dss.2008.07.007

    Article  Google Scholar 

  16. Savaser, S.K., Kara, B.Y.: Mobile healthcare services in rural areas: an application with periodic location routing problem. OR Spectrum 44(3), 875–910 (2022). https://doi.org/10.1007/s00291-022-00670-3

    Article  Google Scholar 

  17. Schneider, M., Drexl, M.: A survey of the standard location-routing problem. Ann. Oper. Res. 259(1–2), 389–414 (2017). https://doi.org/10.1007/s10479-017-2509-0

    Article  MathSciNet  Google Scholar 

  18. George, D., Ronald, S.: Simultaneous Optimization of Several Response Variables. J. Qual. Technol. 12(4), 214–219 (1980). https://doi.org/10.1080/00224065.1980.11980968

    Article  Google Scholar 

  19. Puschmann, T., Alt, R.: Sharing Economy. Bus. Inf. Syst. Eng. 58(1), 93–99 (2016). https://doi.org/10.1007/s12599-015-0420-2

    Article  Google Scholar 

  20. Cheng, M.: Sharing economy: A review and agenda for future research. Int. J. Hosp. Manage. 57, 60–70 (2016). https://doi.org/10.1016/j.ijhm.2016.06.003

    Article  Google Scholar 

  21. Hossain, M.: Sharing economy: A comprehensive literature review. Int. J. Hosp. Manage. 87, 102470 (2020). https://doi.org/10.1016/j.ijhm.2020.102470

    Article  Google Scholar 

  22. Cao, E.R., Chen, G.Z.: Information sharing motivated by production cost reduction in a supply chain with downstream competition. Nav. Res. Logist. 68(7), 898–907 (2021). https://doi.org/10.1002/nav.21977

    Article  MathSciNet  Google Scholar 

  23. Guo, H., Yang, C.C., Liu, B.B., Yang, F.: Performance-based contracts in the sharing economy: A supply chain framework with application of Internet of Things. Ann. Oper. Res. 326(SUPPL 1), 1–1 (2023). https://doi.org/10.1007/s10479-021-04144-7

    Article  MathSciNet  Google Scholar 

  24. Gansterer, M., Hartl, R.F., Tzur, M.: Transportation in the Sharing Economy. Transp. Sci. 56(3), 567–570 (2022). https://doi.org/10.1287/trsc.2022.1143

    Article  Google Scholar 

  25. Choi, T.M., He, Y.Y.: Peer-to-peer collaborative consumption for fashion products in the sharing economy: Platform operations. Transportation research part e-logistics and transportation review 126, 49–65 (2019). https://doi.org/10.1016/j.tre.2019.03.016

    Article  Google Scholar 

  26. Zhou, Z.N., Wan, X.: Does the Sharing Economy Technology Disrupt Incumbents? Exploring the Influences of Mobile Digital Freight Matching Platforms on Road Freight Logistics Firms. Prod. Oper. Manag. 31(1), 117–137 (2022). https://doi.org/10.1111/poms.13491

    Article  Google Scholar 

  27. Li, Y.S., Zhang, G.Z., Pang, Z.B., Li, L.F.: Continuum approximation models for joint delivery systems using trucks and drones. Enterprise Information Systems 14(4), 406–435 (2020). https://doi.org/10.1080/17517575.2018.1536928

    Article  Google Scholar 

  28. Hsu, C.I., Chen, W.T., Wu, W.J.: Optimal delivery cycles for joint distribution of multi-temperature food. Food Control 34(1), 106–114 (2013). https://doi.org/10.1016/j.foodcont.2013.04.003

    Article  Google Scholar 

  29. Ostermeier, M., Henke, T., Hübner, A., Wäscher, G.: Multi-compartment vehicle routing problems: State-of-the-art, modeling framework and future directions. Eur. J. Oper. Res. 292(3), 799–817 (2021). https://doi.org/10.1016/j.ejor.2020.11.009

    Article  MathSciNet  Google Scholar 

  30. Shi, Y., Chen, M., Qu, T., Liu, W., Cai, Y.J.: Digital connectivity in an innovative joint distribution system with real-time demand update. Comput. Indus. 123, 103275 (2020). https://doi.org/10.1016/j.compind.2020.103275

    Article  Google Scholar 

  31. Ouhader, H., El Kyal, M.: Combining Facility Location and Routing Decisions in Sustainable Urban Freight Distribution under Horizontal Collaboration: How Can Shippers Be Benefited? Math. Probl. Eng. 2017, 8687515 (2017). https://doi.org/10.1155/2017/8687515

    Article  MathSciNet  Google Scholar 

  32. Watson-Gandy, C.D.T., Dohrn, P.J.: Depot location with van salesmen — A practical approach. Omega 1(3), 321–329 (1973). https://doi.org/10.1016/0305-0483(73)90108-4

    Article  Google Scholar 

  33. Nagy, G., Salhi, S.: Nested Heuristic Methods for the Location-Routeing Problem. J. Oper. Res. Soc. 47(9), 1166–1174 (1996). https://doi.org/10.1057/jors.1996.144

    Article  Google Scholar 

  34. Lim, A., Wang, F.: Multi-Depot Vehicle Routing Problem: A One-Stage Approach. IEEE Trans. Autom. Sci. Eng. 2(4), 397–402 (2005). https://doi.org/10.1109/tase.2005.853472

    Article  Google Scholar 

  35. Wu, T.-H., Low, C., Bai, J.-W.: Heuristic solutions to multi-depot location-routing problems. Comput. Oper. Res. 29(10), 1393–1415 (2002). https://doi.org/10.1016/S0305-0548(01)00038-7

    Article  Google Scholar 

  36. Nagy, G., Salhi, S.: Location-routing: Issues, models and methods. Eur. J. Oper. Res. 177(2), 649–672 (2007). https://doi.org/10.1016/j.ejor.2006.04.004

    Article  MathSciNet  Google Scholar 

  37. Prodhon, C., Prins, C.: A survey of recent research on location-routing problems [Review]. Eur. J. Oper. Res. 238(1), 1–17 (2014). https://doi.org/10.1016/j.ejor.2014.01.005

    Article  Google Scholar 

  38. Tadaros, M., Migdalas, A.: Bi- and multi-objective location routing problems: classification and literature review. Oper. Res. Int. Journal 22(5), 4641–4683 (2022). https://doi.org/10.1007/s12351-022-00734-w

    Article  Google Scholar 

  39. Belenguer, J.-M., Benavent, E., Prins, C., Prodhon, C., Wolfler Calvo, R.: A Branch-and-Cut method for the Capacitated Location-Routing Problem. Comput. Oper. Res. 38(6), 931–941 (2011). https://doi.org/10.1016/j.cor.2010.09.019

    Article  MathSciNet  Google Scholar 

  40. Baldacci, R., Mingozzi, A., Wolfler Calvo, R.: An exact method for the capacitated location-routing problem. Oper. Res. 59(5), 1284–1296 (2011)

    Article  MathSciNet  Google Scholar 

  41. Prins, C., Prodhon, C., Ruiz, A., Soriano, P., Wolfler Calvo, R.: Solving the Capacitated Location-Routing Problem by a Cooperative Lagrangean Relaxation-Granular Tabu Search Heuristic. Transp. Sci. 41(4), 470–483 (2007). https://doi.org/10.1287/trsc.1060.0187

    Article  Google Scholar 

  42. Özyurt, Z., Aksen, D.: Solving the multi-depot location-routing problem with lagrangian relaxation. Extending the horizons: Advances in computing, optimization, and decision technologies 37, 125–144 (2007)

    Google Scholar 

  43. Contardo, C., Cordeau, J.-F., Gendron, B.: A GRASP+ ILP-based metaheuristic for the capacitated location-routing problem. J Heuristics 20, 1–38 (2014)

    Article  Google Scholar 

  44. Chen, X., Chen, B.: Cost-effective designs of fault-tolerant access networks in communication systems. Networks 53(4), 382–391 (2009)

    Article  MathSciNet  Google Scholar 

  45. Yin, R.Y., Lu, P.X.: A Cluster-First Route-Second Constructive Heuristic Method for Emergency Logistics Scheduling in Urban Transport Networks. Sustainability 14(4), 2301 (2022). https://doi.org/10.3390/su14042301

    Article  Google Scholar 

  46. Lam, M., Mittenthal, J.: Capacitated hierarchical clustering heuristic for multi depot location-routing problems. Int. J. Log. Res. Appl. 16(5), 433–444 (2013)

    Article  Google Scholar 

  47. Schneider, M., Löffler, M.: Large Composite Neighborhoods for the Capacitated Location-Routing Problem. Transp. Sci. 53(1), 301–318 (2019). https://doi.org/10.1287/trsc.2017.0770

    Article  Google Scholar 

  48. Perl, J., Daskin, M.S.: A warehouse location-routing problem. Transport. Res. B 19(5), 381–396 (1985)

    Article  Google Scholar 

  49. Liu, X.T., Zhang, K., Chen, B.K., Zhou, J., Miao, L.X.: Analysis of logistics service supply chain for the one belt and one road initiative of China. Transportation Research Part e-logistics and Transportation Review 117, 23–39 (2018). https://doi.org/10.1016/j.tre.2018.01.019

    Article  Google Scholar 

  50. Jiang, L., Wang, Y., Liu, D.M.: Logistics cost sharing in supply chains involving a third-party logistics provider. CEJOR 24(1), 207–230 (2016). https://doi.org/10.1007/s10100-014-0348-5

    Article  MathSciNet  Google Scholar 

  51. Desrochers, M., Laporte, G.: Improvements and extensions to the Miller-Tucker-Zemlin subtour elimination constraints. Oper. Res. Lett. 10(1), 27–36 (1991). https://doi.org/10.1016/0167-6377(91)90083-2

    Article  MathSciNet  Google Scholar 

  52. Rousseeuw, P.J.: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987). https://doi.org/10.1016/0377-0427(87)90125-7

    Article  Google Scholar 

Download references

Funding

Fundamental Research Funds for the Central Universities, China, Grant ID:2023JBMC005. The authors would like to express his gratitude to the reviewers for their time and expertise.

Author information

Authors and Affiliations

Authors

Contributions

Binghui Qie and Zhiwei Sun wrote the main manuscript text. Xun Weng collected the data and supervised. Minyu Jin contributed data or analysis tools. Zhiwei Sun Performed the analysis. Runfeng Yu and Minyu Jin conceived and designed the analysis. All authors reviewed the manuscript.

Corresponding author

Correspondence to Runfeng Yu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qie, B., Weng, X., Sun, Z. et al. Location-routing and cost-sharing models under joint distribution. Cluster Comput 27, 5879–5891 (2024). https://doi.org/10.1007/s10586-024-04282-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-024-04282-0

Keywords

Navigation