Skip to main content
SpringerLink
Log in

A multi-objective optimization approach to package delivery by the crowd of occupied taxis

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

Taxi crowdsourcing has gained great interest from the logistics industry and academe due to its significant economic and environmental impact. However, existing approaches have several limitations and focus solely on single objective optimization problem. In this paper, we propose a three-stage framework, namely MOOP4PD to improve the existing approaches. Firstly, we propose a DesCloser* pruning algorithm with no limitation on taxi capacity and use A* algorithm to further optimize the delivery routes. Then, a novel multi-objective pruning algorithm, named MDesCloser*, is presented to find the non-dominated set, which contains waiting time window MaxWT and taxi capacity MaxC constraints. Finally, we develop a constraint solving approach to obtain the ideal solution (i.e., MaxWT equals 11 and MaxC equals 6). We evaluate the performance using the data set generated by Brinkhoff road network generator in the city of Luoyang, China. Results show that our approach improve the objectives of success rate, average number of participating taxis, average delivery distance and average delivery time. Especially, MDesCloser* have best performance on the success rate with more than 0.88 and minimize the total waiting time of all packages to 14916.6 time slices if failure in delivering and maximize the average transshipping rate of interchange stations.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Chen W, Mes M, Schutten M (2018) Multi-hop driver-parcel matching problem with time windows. Flex Serv Manuf J 30(3):517–553

    Article  Google Scholar 

  2. Ghilas V, Demir E, Van Woensel T (2016) An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows and scheduled lines. Comput Oper Res 72:12–30

    Article  MathSciNet  Google Scholar 

  3. Rais A, Alvelos F, Carvalho MS (2014) New mixed integer-programming model for the pickup-and-delivery problem with transshipment. Eur J Oper Res 235(3):530–539

    Article  MathSciNet  Google Scholar 

  4. Arslan A, Agatz N, Kroon L et al (2019) Crowdsourced delivery: a dynamic pickup and delivery problem with ad hoc drivers. Transp Sci 53(1):67–99

    Article  Google Scholar 

  5. Chen C, Pan S, Wang Z et al (2017) Using taxis to collect city wide e-commerce reverse flows: a crowdsourcing solution. Int J Prod Res 55(7):1–12

    Article  Google Scholar 

  6. Chen C, Zhang D, Ma X et al (2017) CrowdDeliver: planning city-wide package delivery paths leveraging the crowds of taxis. IEEE Trans Intell Transp Syst 18(6):1478–1496

    Google Scholar 

  7. Li H, Zhang Q (2009) Multi-objective optimization problems with complicated pareto sets, MOEA/D and NSGA-II. IEEE Trans Evol Comput 13(2):284–302

    Article  Google Scholar 

  8. Deb K, Pratap A, Agarwal S et al (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  9. Mokbel M, Alarabi L, Bao J, et al (2014) A demonstration of MNTG—a web-based road network traffic generator. In: IEEE international conference on data engineering, pp 1246–1249

  10. McInerney J, Rogers A, Jennings N (2014) Crowdsourcing physical package delivery using the existing routine mobility of a local population. In: D4D challenge, Boston, pp 447–456

  11. Sadilek A, Krumm J, Horvitz E (2013) Crowdphysics: planned and opportunistic crowdsourcing for physical tasks. In: ICWSM, pp 536–545

  12. Xu Y, Tong Y, Shi Y, et al (2019) An efficient insertion operator in dynamic ridesharing services. In: The 35th international conference on data engineering (ICDE), Macau, pp 1022–1033

  13. Wang J, Ren W, Zhang Z, et al (2018) A hybrid multiobjective memetic algorithm for multiobjective periodic vehicle routing problem with time windows. IEEE Trans Syst Man Cybern 50(11):4732–4745

  14. Kafle N, Zou B, Lin J (2017) Design and modeling of a crowdsource enabled system for urban parcel relay and delivery. Transp Res Part B Methodol 99:62–82

    Article  Google Scholar 

  15. Liu Y, Guo B, Chen C et al (2019) FooDNet: Toward an optimized food delivery network based on spatial crowdsourcing. IEEE Trans Mob Comput 18(6):1288–1301

    Article  Google Scholar 

  16. Chen C, Zhang D, Wang L, et al (2014) TaxiExp: a novel framework for city-wide package express shipping via taxi crowd sourcing. In: IEEE international conference on scalable computing and communications and ITS associated workshops, pp 244–251

  17. Crainic T, Gobbato L, Perboli G et al (2016) Logistics capacity planning: a stochastic bin packing formulation and a progressive hedging metaheuristic. Eur J Oper Res 253(2):404–417

    Article  Google Scholar 

  18. Gehring H, Homberger J (2002) Parallelization of a two-phase metaheuristic for routing problems with time windows. J. Heuristics 8(3):251–276

    Article  Google Scholar 

  19. Ghoseiri K, Ghannadpour SF (2006) Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm. Appl Soft Comput 10(4):1096–1107

    Article  Google Scholar 

  20. Zhou Y, Wang J (2015) A local search-based multiobjective optimization algorithm for multiobjective vehicle routing problem with time windows. IEEE Syst J 9(3):1100–1113

    Article  MathSciNet  Google Scholar 

  21. Wu DQ, Dong M, Li HY et al (2016) Vehicle routing problem with time windows using multi-objective co-evolutionary approach. Int J Simul Model 15(4):742–753

    Article  Google Scholar 

  22. Luo J, Li X, Chen MR et al (2015) A novel hybrid shuffied frog leaping algorithm for vehicle routing problem with time windows. Inf Sci 316:266–292

    Article  Google Scholar 

  23. Taha YE, Ayob M, Zakree Ahmad Nazri M et al (2017) An adaptive hybrid algorithm for vehicle routing problems with time windows. Comput Ind Eng 113:382–391

    Article  Google Scholar 

  24. Dong W, Zhou K, Qi H et al (2018) A tissue p system based evolutionary algorithm for multi-objective vrptw. Swarm Evol Comput 39:310–322

    Article  Google Scholar 

  25. Liu R, Xie X, Augusto V et al (2013) Heuristic algorithms for a vehicle routing problem with simultaneous delivery and pickup and time windows in home health care. Eur J Oper Res 230(3):475–486

    Article  MathSciNet  Google Scholar 

  26. Qu B, Zhu Y, Jiao YC et al (2018) A survey on multi-objective evolutionary algorithms for the solution of the environmental/economic dispatch problems. Swarm Evol Comput 38(1):1–11

    Article  Google Scholar 

  27. Chen R, Guo S, Wang X et al (2019) Fusion of multi-RSMOTE with fuzzy integral to classify bug reports with an imbalanced severity distribution. IEEE Trans Fuzzy Syst 27(12):2406–2420

    Article  Google Scholar 

  28. Wang J, Zhou Y, Wang Y et al (2016) Multiobjective vehicle routing problems with simultaneous delivery and pickup and time windows: formulation, instances, and algorithms. IEEE Trans Syst Man Cybern 46(3):582–594

    MathSciNet  Google Scholar 

  29. Pohl I (2014) Bi-directional search. IBM TJ Watson Research Center 1970

  30. Wu L, Xiao X, Deng D, et al (2012) Shortest path and distance queries on road networks: an experimental evaluation. In: PVLDB, pp 406–417

Download references

Funding

This work is supported by the National Natural Science Foundation of China (Nos. 61672122, 61902050, 61906027), the Fundamental Research Funds for the Central Universities (No. 3132019355), the Next-Generation Internet Innovation Project of CERNET (No. NGII20181205), and the China Postdoctoral Science Foundation Funded Project (Grant No. 2019M661080).

Author information

Authors and Affiliations

  1. College of Information Science and Technology, Dalian Maritime University, Linghai Road No.1, Dalian, 116026, Liaoning, China

    Zhifeng Zhou, Rong Chen, Jian Gao & Hu Xing

Authors
  1. Zhifeng Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jian Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hu Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rong Chen.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Z., Chen, R., Gao, J. et al. A multi-objective optimization approach to package delivery by the crowd of occupied taxis. Knowl Inf Syst 64, 2713–2736 (2022). https://doi.org/10.1007/s10115-022-01722-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-022-01722-4

Keywords

Search

Navigation