Skip to main content
SpringerLink
Log in

Research on Delivery Order Scheduling and Delivery Algorithms

  • Conference paper
  • First Online:
Data Science (ICPCSEE 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1880))

  • 243 Accesses

Abstract

Online takeout has become the dining style of most urban residents. With the development of takeout industry, takeout delivery efficiency and customer satisfaction have attracted more and more attention from the industry and academia. The essence of takeout delivery problem is a vehicle routing problem under various constraints. Designing reasonable order dispatching and routing algorithms will help to improve the delivery efficiency and service quality for takeout platform. Firstly, by analyzing the order dispatching and delivery problem itself, we summarize the characteristics of the problem, compare the theoretical research of related problems, and describe it as a problem with the characteristics of soft time window, vehicle capacity limitation, dynamic order and 1–1 pick-up and delivery. Secondly, the problem scenario is described in detail, and the main elements and constraints of the problem are explained. Based on this, aiming at minimizing the overdue time, we divide the problem into two subtasks: order dispatching and routing, and propose an order dispatching strategy considering couriers’ route and a two-stage routing algorithm. Specifically, this paper improves the calculation the cost in the previous heuristic algorithm based on the cost, that is, when dispatching an order, we consider whether there are orders close enough to the source location or destination location of the new order in every courier’s route, and if so, he can pickup and delivery the new order incidentally. In addition, it is also necessary to punish the courier who have long routes and whose orders have large overdue time; When planning the path for each order, we firstly apply the nearest neighbor greedy algorithm for initial routing, which means iteratively find the time-consuming nearest feasible node in the remaining unplanned nodes, and then apply the tabu search algorithm to optimize the initial path. Finally, the static scenario and dynamic scenario are designed to verify the above algorithm. The static problem scenario solution verifies the effectiveness of the two-stage routing algorithm. The dynamic problem scenario proves the effectiveness of the order dispatching strategy considering couriers’ route. We also analyze the ratio of the number of orders and couriers’ influence on the performance of the algorithm, and recommend the quantity of couriers under the demand of orders with specific quantity. The experimental results can provide suggestions for the management of courier under the background of epidemic prevention measures such as closing-down due to the COVID-19. In addition, we propose that in a specific scenario, the order re-dispatching can make great improvement at small cost of computing resources.

This paper is supported by Engineering Research Center of State Financial Security, Ministry of Education, Central University of Finance and Economics, Beijing, 102206, China.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Na, D., Zhang, J.: Study on assign mode of o2o takeaway order delivery tasks. Shanghai Manag. Sci. 40(01), 63–66 (2018)

    Google Scholar 

  2. Ma, S., Zheng, Y., Wolfson, O.: Real-time city-scale taxi ridesharing. IEEE Trans. Knowl. Data Eng. 27(7), 1782–1795 (2014)

    Article  Google Scholar 

  3. Asghari, M., Deng, D., Shahabi, C., et al.: Price-aware real-time ride-sharing at scale: an auction-based approach. In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp.  1–10 (2016)

    Google Scholar 

  4. Lin. Q., Deng, L., Sun, J., et al.: Optimal demand-aware ride-sharing routing. In: IEEE INFOCOM 2018-IEEE Conference on Computer Communications, pp.  2699–2707. IEEE (2018)

    Google Scholar 

  5. Jiang, W., Dominguez, C.R., Zhang, P., et al.: Large-scale nationwide ridesharing system: A case study of Chunyun. Int. J. Trans. Sci. Technol. 7(1), 45–59 (2018)

    Article  Google Scholar 

  6. Zheng Wang, Tingyu Li, Caifan Yue. Online scheduling for the instant delivery problem in a city based on multiple prediction scenarios.  38(12), 3197–3211 (2018)

    Google Scholar 

  7. Chen, D., Chen, T., Jiang, S., Zhang, C., Wang, C., Lu, M.: Optimal model of chicken distribution vehicle scheduling based on order clustering. Smart Agricul 2(4), 137–148 (2020)

    Google Scholar 

  8. Zhang, Y., Liu, Y., Li, G., et al.: Route prediction for instant delivery. Proceedings of the ACM  Intera. Mobile, Wearable  Ubiquitous Technol. 3(3), 1–25 (2019)

    Google Scholar 

  9. Jahanshahi, H., Bozanta, A., Cevik, M., et al.: A deep reinforcement learning approach for the meal delivery problem. Knowl.-Based Syst. 243, 108489 (2022)

    Article  Google Scholar 

  10. Guo, B., Wang, S., Ding, Y., et al.: concurrent order dispatch for instant delivery with time-constrained actor-critic reinforcement learning. In: 2021 IEEE Real-Time Systems Symposium (RTSS), pp. 176–187. IEEE (2021)

    Google Scholar 

  11. Bozanta, A., Cevik, M., Kavaklioglu, C., et al.: Courier routing and assignment for food delivery service using reinforcement learning. Comput. Ind. Eng. 164, 107871 (2022)

    Article  Google Scholar 

  12. Zhou, M., Jin, J., Zhang, W., et al.: Multi-agent reinforcement learning for order-dispatching via order-vehicle distribution matching. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 2645–2653 (2019)

    Google Scholar 

  13. Xu, Z., Li, Z., Guan, Q., et al.: Large-scale order dispatch in on-demand ride-hailing platforms: A learning and planning approach. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 905–913 (2018)

    Google Scholar 

  14. Dantzig, G.B., Ramser, J.H.: The truck dispatching problem. Manage. Sci. 6(1), 80–91 (1959)

    Article  MathSciNet  MATH  Google Scholar 

  15. Battarra, M., Cordeau, J.F., Iori. M.: Chapter 6: pickup-and-delivery problems for goods transportation. In: Vehicle Routing: Problems, Methods, and Applications, Second Edition. Society for Industrial and Applied Mathematics, pp. 161–191 (2014)

    Google Scholar 

  16. Angelelli, E., Mansini, R.: The vehicle routing problem with time windows and simultaneous pick-up and delivery. In: Quantitative Approaches to Distribution Logistics and Supply Chain Management, pp. 249-267. Springer, Berlin (2002). https://doi.org/10.1007/978-3-642-56183-2_15

  17. Qu, Y., Bard, J.F.: The heterogeneous pickup and delivery problem with configurable vehicle capacity. Trans. Res. Part C: Emerging Technol. 32, 1–20 (2013)

    Article  Google Scholar 

  18. Nagy, G., Salhi, S.: Heuristic algorithms for single and multiple depot vehicle routing problems with pickups and deliveries. Eur. J. Oper. Res. 162(1), 126–141 (2005)

    Article  MATH  Google Scholar 

  19. Wang, C., Qiu, Y.: Vehicle routing problem with stochastic demands and simultaneous delivery and pickup based on the cross-entropy method. In: Advances in Automation and Robotics, vol. 2, pp. 55-60. Springer, Berlin, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25646-2_7

    Book  Google Scholar 

  20. Pang, Y., Luo, H., Xing, L., Ren, T.: A survey of vehicle routing problems and solution methods. Control Theory Appl. 36(10), 1573–1584 (2019)

    MATH  Google Scholar 

  21. Kalantari, B., Hill, A.V., Arora, S.R.: An algorithm for the traveling salesman problem with pickup and delivery customers. Eur. J. Oper. Res. 22(3), 377–386 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  22. Desrosiers, J., Dumas, Y., Soumis, F.: A dynamic programming solution of the large-scale single-vehicle dial-a-ride problem with time windows. Am. J. Math. Manag. Sci. 6(3–4), 301–325 (1986)

    MATH  Google Scholar 

  23. Gajpal, Y., Abad, P.: Saving-based algorithms for vehicle routing problem with simultaneous pickup and delivery. J. Operat. Res. Soc. 61(10), 1498–1509 (2010)

    Article  MATH  Google Scholar 

  24. Jun, Y., Kim, B.I.: New best solutions to VRPSPD benchmark problems by a perturbation based algorithm. Expert Syst. Appl. 39(5), 5641–5648 (2012)

    Article  MathSciNet  Google Scholar 

  25. Zheng, H., Wang, S., Cha, Y., et al.: A two-stage fast heuristic for food delivery route planning problem. In: Informs annual meeting, Seattle, Washington, USA (2019)

    Google Scholar 

  26. Chen, W., Dai, S.: A survey of algorithms for vehicle routing problems. J. Chuzhou Univ. 03, 19–25 (2007)

    Google Scholar 

  27. Chen, J.F., Wu, T.H.: Vehicle routing problem with simultaneous deliveries and pickups. J. Operat. Res. Soc. 57(5), 579–587 (2006)

    Article  MATH  Google Scholar 

  28. Hosny, M.I., Mumford, C.L.: The single vehicle pickup and delivery problem with time windows: intelligent operators for heuristic and metaheuristic algorithms. J. Heuristics 16(3), 417–439 (2010)

    Article  MATH  Google Scholar 

  29. Tasan, A.S., Gen, M.: A genetic algorithm based approach to vehicle routing problem with simultaneous pick-up and deliveries. Comput. Ind. Eng. 62(3), 755–761 (2012)

    Article  Google Scholar 

  30. Demir, E., Bektaş, T., Laporte, G.: An adaptive large neighborhood search heuristic for the pollution-routing problem. Eur. J. Oper. Res. 223(2), 346–359 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  31. Su, X., Qin, H., Wang, K.: Tabu search algorithm for vehicle routing problems with time windows and multiple delivery personnel. J. Chongqing Normal Univ. (Nat. Sci.), 37(01), 22–30 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information, Central University of Finance and Economics, Beijing, China

    Qian Hong & Yue Wang

Authors
  1. Qian Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yue Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yue Wang .

Editor information

Editors and Affiliations

  1. Harbin Engineering University, Harbin, China

    Zhiwen Yu

  2. Harbin Engineering University, Harbin, China

    Qilong Han

  3. Harbin Institute of Technology, Harbin, Heilongjiang, China

    Hongzhi Wang

  4. Northwestern Polytechnical University, Xi'an, China

    Bin Guo

  5. Shiga University, Shiga, Japan

    Xiaokang Zhou

  6. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  7. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hong, Q., Wang, Y. (2023). Research on Delivery Order Scheduling and Delivery Algorithms. In: Yu, Z., et al. Data Science. ICPCSEE 2023. Communications in Computer and Information Science, vol 1880. Springer, Singapore. https://doi.org/10.1007/978-981-99-5971-6_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-5971-6_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-5970-9

  • Online ISBN: 978-981-99-5971-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation