IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v104y2021ics0305048321000918.html
   My bibliography  Save this article

Managing price and fleet size for courier service with shared drones

Author

Listed:
  • Pei, Zhi
  • Dai, Xu
  • Yuan, Yilun
  • Du, Rui
  • Liu, Changchun

Abstract

With the rapid development of modern logistics systems, the unmanned aerial vehicle (a.k.a. drone) based delivery service emerges as a technology-driven innovation, and it is now pilot running in many regions across the globe. The drone delivery system aims to reduce the labor cost in the current labor-intensive courier industry, as well as avoid the disturbance caused by geographic and demographic features. For the drone delivery service providers to survive and prosper, a pool of shared drones is integrated into the ecological chain, where the courier service providers focus on the delivery operations. For such a drone sharing system, revenue management becomes vital in terms of pricing, drone hiring cost, and service-related cost. To better depict the system dynamics, in the present paper, a time-varying and price-sensitive queueing model is formulated, where the customer behaviors are taken into account, such as balking and abandonment. To guarantee a preset service level, the probability of abandonment and expected delay are considered as the control targets, and the pricing for courier service and the maintained fleet size are considered simultaneously. For the low and moderate quality of service(QoS) target, a fluid control model is constructed with a closed-form solution. For the high QoS target, a modified approximation algorithm is designed to numerically tackle the problem. Based on the simulation, it is observed that the proposed approximation methods not only provide a high-quality joint strategy but also help stabilize the system performance. In addition, only mild price change is needed to reach the optimal condition, and a time lag exists between the optimal fleet sizing and the demand variation, which may shed light on the demand-driven fleet sizing under more general settings.

Suggested Citation

  • Pei, Zhi & Dai, Xu & Yuan, Yilun & Du, Rui & Liu, Changchun, 2021. "Managing price and fleet size for courier service with shared drones," Omega, Elsevier, vol. 104(C).
  • Handle: RePEc:eee:jomega:v:104:y:2021:i:c:s0305048321000918
    DOI: 10.1016/j.omega.2021.102482
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0305048321000918
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.omega.2021.102482?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Maenhout, Broos & Vanhoucke, Mario, 2013. "An integrated nurse staffing and scheduling analysis for longer-term nursing staff allocation problems," Omega, Elsevier, vol. 41(2), pages 485-499.
    2. Ward Whitt, 2005. "Engineering Solution of a Basic Call-Center Model," Management Science, INFORMS, vol. 51(2), pages 221-235, February.
    3. Parmentier, Axel & Meunier, Frédéric, 2020. "Aircraft routing and crew pairing: Updated algorithms at Air France," Omega, Elsevier, vol. 93(C).
    4. Terry A. Taylor, 2018. "On-Demand Service Platforms," Manufacturing & Service Operations Management, INFORMS, vol. 20(4), pages 704-720, October.
    5. Seidl, Andrea & Kaplan, Edward H. & Caulkins, Jonathan P. & Wrzaczek, Stefan & Feichtinger, Gustav, 2016. "Optimal control of a terror queue," European Journal of Operational Research, Elsevier, vol. 248(1), pages 246-256.
    6. Philipp Afèche & J. Michael Pavlin, 2016. "Optimal Price/Lead-Time Menus for Queues with Customer Choice: Segmentation, Pooling, and Strategic Delay," Management Science, INFORMS, vol. 62(8), pages 2412-2436, August.
    7. Anton Braverman & J. G. Dai & Xin Liu & Lei Ying, 2019. "Empty-Car Routing in Ridesharing Systems," Operations Research, INFORMS, vol. 67(5), pages 1437-1452, September.
    8. Niyirora, Jerome & Zhuang, Jun, 2017. "Fluid approximations and control of queues in emergency departments," European Journal of Operational Research, Elsevier, vol. 261(3), pages 1110-1124.
    9. Ping Cao & Yaolei Wang & Jingui Xie, 2019. "Priority Service Pricing with Heterogeneous Customers: Impact of Delay Cost Distribution," Production and Operations Management, Production and Operations Management Society, vol. 28(11), pages 2854-2876, November.
    10. Liu, Yunan & Whitt, Ward, 2017. "Stabilizing performance in a service system with time-varying arrivals and customer feedback," European Journal of Operational Research, Elsevier, vol. 256(2), pages 473-486.
    11. Restrepo, María I. & Rousseau, Louis-Martin & Vallée, Jonathan, 2020. "Home healthcare integrated staffing and scheduling," Omega, Elsevier, vol. 95(C).
    12. Jerome Niyirora & Jamol Pender, 2016. "Optimal staffing in nonstationary service centers with constraints," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(8), pages 615-630, December.
    13. Baric{s} Ata & Shiri Shneorson, 2006. "Dynamic Control of an M/M/1 Service System with Adjustable Arrival and Service Rates," Management Science, INFORMS, vol. 52(11), pages 1778-1791, November.
    14. Chihoon Lee & Amy R. Ward, 2019. "Pricing and Capacity Sizing of a Service Facility: Customer Abandonment Effects," Production and Operations Management, Production and Operations Management Society, vol. 28(8), pages 2031-2043, August.
    15. Schwarz, Justus Arne & Selinka, Gregor & Stolletz, Raik, 2016. "Performance analysis of time-dependent queueing systems: Survey and classification," Omega, Elsevier, vol. 63(C), pages 170-189.
    16. Philipp Afèche & Opher Baron & Joseph Milner & Ricky Roet-Green, 2019. "Pricing and Prioritizing Time-Sensitive Customers with Heterogeneous Demand Rates," Operations Research, INFORMS, vol. 67(4), pages 1184-1208, July.
    17. Bai, Jiaru & So, Kut C. & Tang, Christopher, 2016. "A queueing model for managing small projects under uncertainties," European Journal of Operational Research, Elsevier, vol. 253(3), pages 777-790.
    18. Stefan Poikonen & Bruce Golden & Edward A. Wasil, 2019. "A Branch-and-Bound Approach to the Traveling Salesman Problem with a Drone," INFORMS Journal on Computing, INFORMS, vol. 31(2), pages 335-346, April.
    19. Yunan Liu & Ward Whitt, 2012. "Stabilizing Customer Abandonment in Many-Server Queues with Time-Varying Arrivals," Operations Research, INFORMS, vol. 60(6), pages 1551-1564, December.
    20. Niels Agatz & Paul Bouman & Marie Schmidt, 2018. "Optimization Approaches for the Traveling Salesman Problem with Drone," Transportation Science, INFORMS, vol. 52(4), pages 965-981, August.
    21. Mattia, Sara & Rossi, Fabrizio & Servilio, Mara & Smriglio, Stefano, 2017. "Staffing and scheduling flexible call centers by two-stage robust optimization," Omega, Elsevier, vol. 72(C), pages 25-37.
    22. Wang, Zheng & Sheu, Jiuh-Biing, 2019. "Vehicle routing problem with drones," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 350-364.
    23. Defraeye, Mieke & Van Nieuwenhuyse, Inneke, 2016. "Staffing and scheduling under nonstationary demand for service: A literature review," Omega, Elsevier, vol. 58(C), pages 4-25.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wu, Haotian & Savelsbergh, Martin & Huang, Yixiao, 2022. "Planning the city operations of a parcel express company," Omega, Elsevier, vol. 107(C).
    2. Yi Li & Min Liu & Dandan Jiang, 2022. "Application of Unmanned Aerial Vehicles in Logistics: A Literature Review," Sustainability, MDPI, vol. 14(21), pages 1-18, November.
    3. Dell’Amico, Mauro & Montemanni, Roberto & Novellani, Stefano, 2021. "Algorithms based on branch and bound for the flying sidekick traveling salesman problem," Omega, Elsevier, vol. 104(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Niyirora, Jerome & Zhuang, Jun, 2017. "Fluid approximations and control of queues in emergency departments," European Journal of Operational Research, Elsevier, vol. 261(3), pages 1110-1124.
    2. Nils Boysen & Stefan Fedtke & Stefan Schwerdfeger, 2021. "Last-mile delivery concepts: a survey from an operational research perspective," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(1), pages 1-58, March.
    3. Jiaqi Zhou & Ilya O. Ryzhov, 2021. "Equilibrium analysis of observable express service with customer choice," Queueing Systems: Theory and Applications, Springer, vol. 99(3), pages 243-281, December.
    4. De Munck, Thomas & Chevalier, Philippe & Tancrez, Jean-Sébastien, 2023. "Managing priorities on on-demand service platforms with waiting time differentiation," International Journal of Production Economics, Elsevier, vol. 266(C).
    5. Cheng, Chun & Adulyasak, Yossiri & Rousseau, Louis-Martin, 2020. "Drone routing with energy function: Formulation and exact algorithm," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 364-387.
    6. Tiniç, Gizem Ozbaygin & Karasan, Oya E. & Kara, Bahar Y. & Campbell, James F. & Ozel, Aysu, 2023. "Exact solution approaches for the minimum total cost traveling salesman problem with multiple drones," Transportation Research Part B: Methodological, Elsevier, vol. 168(C), pages 81-123.
    7. Zhang, Guowei & Zhu, Ning & Ma, Shoufeng & Xia, Jun, 2021. "Humanitarian relief network assessment using collaborative truck-and-drone system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    8. Cho, David D. & Stauffer, Jon M., 2022. "Tele-medicine question response service: Analysis of benefits and costs," Omega, Elsevier, vol. 111(C).
    9. Defraeye, Mieke & Van Nieuwenhuyse, Inneke, 2016. "Staffing and scheduling under nonstationary demand for service: A literature review," Omega, Elsevier, vol. 58(C), pages 4-25.
    10. Xia, Yang & Zeng, Wenjia & Zhang, Canrong & Yang, Hai, 2023. "A branch-and-price-and-cut algorithm for the vehicle routing problem with load-dependent drones," Transportation Research Part B: Methodological, Elsevier, vol. 171(C), pages 80-110.
    11. Eugene Furman & Adam Diamant & Murat Kristal, 2021. "Customer Acquisition and Retention: A Fluid Approach for Staffing," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 4236-4257, November.
    12. Roberto Roberti & Mario Ruthmair, 2021. "Exact Methods for the Traveling Salesman Problem with Drone," Transportation Science, INFORMS, vol. 55(2), pages 315-335, March.
    13. Zhou, Hang & Qin, Hu & Cheng, Chun & Rousseau, Louis-Martin, 2023. "An exact algorithm for the two-echelon vehicle routing problem with drones," Transportation Research Part B: Methodological, Elsevier, vol. 168(C), pages 124-150.
    14. Jiang, Jie & Dai, Ying & Yang, Fei & Ma, Zujun, 2024. "A multi-visit flexible-docking vehicle routing problem with drones for simultaneous pickup and delivery services," European Journal of Operational Research, Elsevier, vol. 312(1), pages 125-137.
    15. Yu, Shaohua & Puchinger, Jakob & Sun, Shudong, 2020. "Two-echelon urban deliveries using autonomous vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    16. Yu, Shaohua & Puchinger, Jakob & Sun, Shudong, 2022. "Van-based robot hybrid pickup and delivery routing problem," European Journal of Operational Research, Elsevier, vol. 298(3), pages 894-914.
    17. Archetti, Claudia & Peirano, Lorenzo & Speranza, M. Grazia, 2022. "Optimization in multimodal freight transportation problems: A Survey," European Journal of Operational Research, Elsevier, vol. 299(1), pages 1-20.
    18. Liu, Zeyu & Li, Xueping & Khojandi, Anahita, 2022. "The flying sidekick traveling salesman problem with stochastic travel time: A reinforcement learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    19. William A. Massey & Jamol Pender, 2018. "Dynamic rate Erlang-A queues," Queueing Systems: Theory and Applications, Springer, vol. 89(1), pages 127-164, June.
    20. Yichen Lu & Chao Yang & Jun Yang, 2022. "A multi-objective humanitarian pickup and delivery vehicle routing problem with drones," Annals of Operations Research, Springer, vol. 319(1), pages 291-353, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jomega:v:104:y:2021:i:c:s0305048321000918. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.