Skip to main content
Log in

An Approximation of the Customer Waiting Time for Online Restaurants Owning Delivery System

  • Published:
Journal of Systems Science and Complexity Aims and scope Submit manuscript

Abstract

Online restaurants, which receive online orders and deliver food directly to the customer’s residence, are becoming increasingly popular. To be successful, online restaurants need to provide reliable and prompt deliveries. Careful design of the meal preparation and order delivery systems is needed to avoid excessive customer waiting time between ordering and delivery. This paper considers the meal preparation and delivery processes simultaneously to approximate average customer waiting time for deliveries. The authors first discuss the system performance with one cook and unit-capacity delivery vehicles, using an M/G/1 queue and a GI/G/1 queue. Numerical experiments show that our approximation can adequately describe real waiting times. Then, series queues with multiple cooks and multi-capacity delivery vehicles, e.g., an M/G/n queue and a GI/Gn/1 queue, are examined. Results show that except for situations with a large meal preparation time and a small vehicle capacity, compared with the result of simulation, the approximation in this paper is acceptable with a deviation of less than 20%. The marginal decrease in waiting time associated with hiring more vehicles is estimated under different meal preparation speeds, sizes of service area and vehicle capacities.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. iiMedia Research China. Online restaurant research report for China in 2017 Q1, 2017, https://doi.org/http://www.iimedia.cn/51210.html.

  2. Yeo V C S, Goh S K, and Rezaei S, Consumer experiences, attitude and behavioral intention toward online food delivery (OFD) services, Journal of Retailing and Consumer Services, 2017, 35, 150–162.

    Article  Google Scholar 

  3. Fancello G, Paddeu D, and Fadda P, Investigating last food mile deliveries: A case study approach to identify needs of food delivery demand, Research in Transportation Economics, 2017, 65: 56–66.

    Article  Google Scholar 

  4. Giannikas E, Using customer-related data to enhance e-grocery home delivery, Industrial Management and Data Systems, 2017, 117(9), 1917–1933.

    Article  Google Scholar 

  5. Siregar B, Gunawan D, Andayani U, et al., Food delivery system with the utilization of vehicle using geographical information system (GIS) and a star algorithm, Journal of Physics Conference Series, 2017, 801, 012038.

    Article  Google Scholar 

  6. Dayama N R and Krishnamoorthy M, Facility location and routing decisions for a food delivery network, IEEE International Conference on Industrial Engineering and Engineering Management, 2016, 94–98.

    Google Scholar 

  7. Jayakumar Nair D, Grzybowska H, Rey D, et al., Food rescue and delivery: Heuristic algorithm for periodic unpaired pickup and delivery vehicle routing problem, Trasportation Research Board, 2016, DOI: 10.3141/2548-10.

    Google Scholar 

  8. Song B D and Ko Y D, A vehicle routing problem of both refrigerated- and general-type vehicles for perishable food products delivery, Journal of Food Engineering, 2016, 169, 61–71.

    Article  Google Scholar 

  9. Deng N and Zhang J, Study on assign mode of O2O takeaway order delivery tasks, Shanghai Management Science, 2018, 40(1): 63–66.

    Google Scholar 

  10. Wang S, Zhao L, and Hu Q, Vehicle routing problem with O2O takeout delivery based on stochastic travel times, Logistics Sci-Tech, 2017, 1: 93–101.

    Google Scholar 

  11. Croes G A, A method for solving traveling-salesman problems, Operations Research, 1958, 6(6): 791–812.

    Article  MathSciNet  MATH  Google Scholar 

  12. Golden B, Raghavan S, and Wasil E, The Vehicle Routing Problem: Latest Advances and New Challenges, Springer, US, 2008.

    Book  MATH  Google Scholar 

  13. Bertsimas D J and Garrett V R, A stochastic and dynamic vehicle routing problem in the Euclidean plane, Operations Research, 1991, 39(4): 603–615.

    Article  MATH  Google Scholar 

  14. Bertsimas D J and Ryzin G V, Stochastic and dynamic vehicle routing in the Euclidean plane with multiple capacitated vehicles, Operations Research, 1993, 41(1): 60–76.

    Article  MathSciNet  MATH  Google Scholar 

  15. Du T C, Li E Y, and Chou D, Dynamic vehicle routing for online B2C delivery, Omega, 2005, 33(1): 33–45.

    Article  Google Scholar 

  16. Molenbruch Y, Braekers K, and Caris A, Typology and literature review for dial-a-ride problems, Annals of Operations Research, 2017, 259(1–2): 295–325.

    Article  MathSciNet  MATH  Google Scholar 

  17. Santos D O and Xavier E C, Taxi and ride sharing: A dynamic dial-a-ride problem with money as an incentive, Expert Systems with Applications, 2015, 42(19): 6728–6737.

    Article  Google Scholar 

  18. Muelas S, LaTorre A, and Pena J M,. A variable neighborhood search algorithm for the optimization of a dial-a-ride problem in a large city, Expert Systems with Applications, 2013, 40(14): 5516–5531.

    Article  Google Scholar 

  19. Baldacci R, Maniezzo V, and Mingozzi A, An exact method for the car pooling problem based on lagrangean column generation, Operations Research, 2004, 52(3): 422–439.

    Article  MATH  Google Scholar 

  20. Teal R F, Carpooling: Who, how and why, Transportation Research Part A: General, 1987, 21(3), 203–214.

    Article  Google Scholar 

  21. Anderson J E, A review of the state of the art of personal rapid transit, Journal of Advanced Transportation, 2000, 34(1): 3–29.

    Article  Google Scholar 

  22. Wang H, Routing and scheduling for a last-mile transportation system, Transportation Science, 2017, DOI: 10.1287/trsc.2017.0753.

    Google Scholar 

  23. Punakivi M, YrjoÉlaÉ H, and HolmstroÉm J, Solving the last mile issue: Reception box or delivery box?, International Journal of Physical Distribution & Logistics Management, 2001, 31(6): 427–439.

    Article  Google Scholar 

  24. Lee H L and Whang S, Winning the last mile of e-commerce, MIT Sloan Management Review, 2001, 42(4): 54–62.

    Google Scholar 

  25. Esper T L, Jensen T D, Turnipseed F L, et al., The last mile: An examination of effects of online retail delivery strategies on consumers, Journal of Business Logistics, 2003, 24(2): 177–203.

    Article  Google Scholar 

  26. Boyer K K, Prud’homme A M, and Chung W, The last mile challenge: Evaluating the effects of customer density and delivery window patterns, Journal of Business Logistics, 2009, 30(1): 185–201.

    Article  Google Scholar 

  27. Song L, Cherrett T, McLeod F, et al., Addressing the last mile problem: Transport impacts of collection and delivery points, Transportation Research Record: Journal of the Transportation Research Board, 2009, 45(2097): 9–18.

    Article  Google Scholar 

  28. Gross D, Fundamentals of Queueing Theory, 4th Ed., John Wiley & Sons, New Jersey, 2008.

    Book  MATH  Google Scholar 

  29. Afeche P and Pavlin M, Optimal price-lead time menus for queues with customer choice: Priorities, pooling & strategic delay, Management Science, 2016, 62(1): 2412–2436.

    Article  Google Scholar 

  30. Hayel Y, Quadri D, Jiménez T, et al., Decentralized optimization of last-mile delivery services with non-cooperative bounded rational customers, Annals of Operations Research, 2016, 239(2): 451–469.

    Article  MathSciNet  MATH  Google Scholar 

  31. Zhou L, Wang X, Ni L, et al., Location-routing problem with simultaneous home delivery and customer’s pickup for city distribution of online shopping purchases, Sustainability, 2016, 8(8): 828–847.

    Article  Google Scholar 

  32. Olbert H, Protopappa-Sieke M, and Thonemann U W, Analyzing the effect of express orders on supply chain costs and delivery times. Production & Operations Management, 2016, 25(12): 2035–2050.

    Article  Google Scholar 

  33. Swihart M R and Papastavrou J D, A stochastic and dynamic model for the single-vehicle pick-up and delivery problem, European Journal of Operational Research, 1999, 114(3): 447–464.

    Article  MATH  Google Scholar 

  34. Wang H and Odoni A, Approximating the performance of a “last mile” transportation system, Transportation Science, 2014, 50(2): 659–675.

    Article  Google Scholar 

  35. Alfa A S, Applied Discrete-Time Queues, Springer, New York, 2016.

    Book  MATH  Google Scholar 

  36. Whitt W, Approximations for departure processes and queues in series, Naval Research Logistics (NRL), 1984, 31(4): 499–521.

    Article  MathSciNet  MATH  Google Scholar 

  37. Daley D J, The correlation structure of the output process of some single server queueing systems, The Annals of Mathematical Statistics, 1968, 39(3): 1007–1019.

    Article  MathSciNet  MATH  Google Scholar 

  38. Makino T, On a study of output distribution, Journal of the Operational Research Society, 1966, 8: 109–133.

    MathSciNet  MATH  Google Scholar 

  39. Raghavendran C H V, Satish G N, Sundari M R, et al., Tandem communication network model with DBA having non homogeneous Poisson arrivals and feedback for first node, International Journal of Computers and Technology, 2014, 13(9): 621–625.

    Article  Google Scholar 

  40. Wu K and Zhao N, Analysis of dual tandem queues with a finite buffer capacity and nonoverlapping service times and subject to breakdowns, IIE Transactions, 2015, 47(12): 1329–1341.

    Article  Google Scholar 

  41. Wu K, Zhao N, and Lee C K M, Queue time approximations for a cluster tool with job cascading, IEEE Transactions on Automation Science and Engineering, 2016, 13(2): 1200–1206.

    Article  Google Scholar 

  42. Zhou W, Zhang R, and Zhou Y, A queuing model on supply chain with the form postponement strategy, Computers & Industrial Engineering, 2013, 66(4): 643–652.

    Article  Google Scholar 

  43. Lee Y J and Zipkin P, Tandem queues with planned inventories, Operations Research, 1992, 40(5): 936–947.

    Article  MATH  Google Scholar 

  44. Kraemer W and Langenbach-Belz M, Approximate formulae for the delay in the queueing system GI/G/1, Proceedings of the 8th International Teletraffic Congress, 1976, 2(3): 2351–2358.

    Google Scholar 

  45. Whitt W, Approximations for the GI/G/m queue, Production & Operations Management, 1993, 2(2): 114–161.

    Article  Google Scholar 

  46. Halfin S and Whitt W, Heavy-Traffic Limits for Queues with Many Exponential Servers, Informs, 1981.

    Book  MATH  Google Scholar 

  47. Johnson D S, McGeoch L A, and Rothberg E E, Asymptotic experimental analysis for the Held-CKarp traveling salesman bound, Proceedings of the 7th Annual ACM-SIAM Symposium on Discrete Algorithms, 1996, 341–350.

    Google Scholar 

  48. Beardwood J, Halton J H, and Hammersley J M, The shortest path through many points, Mathematical Proceedings of the Cambridge Philosophical Society, Cambridge University Press, 1959, 55(4): 299–327.

    MATH  Google Scholar 

  49. Gremlich R, Hamfelt A, and Valkovsky V, Prediction of the optimal decision distribution for the traveling salesman problem, Proceedings of IPSI International Conf., 2004.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fu Zhao.

Additional information

This research was supported by the National Natural Science Foundation of China under Grant No. 71661167009, the Fundamental Research Funds for the Central Universities under Grant No. B17JB00280.

This paper was recommended for publication by Editor WANG Shouyang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, T., Zhao, F., Zhang, J. et al. An Approximation of the Customer Waiting Time for Online Restaurants Owning Delivery System. J Syst Sci Complex 32, 907–931 (2019). https://doi.org/10.1007/s11424-018-7316-4

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11424-018-7316-4

Keywords

Navigation