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The adoption of self-driving delivery robots in last mile logistics

Author

Listed:
  • Chen, Cheng
  • Demir, Emrah
  • Huang, Yuan
  • Qiu, Rongzu

Abstract

Covid-19, the global pandemic, has taught us the importance of contactless delivery service and robotic automation. Using self-driving delivery robots can provide flexibility for on-time deliveries and help better protect both driver and customers by minimizing contact. To this end, this paper introduces a new vehicle routing problem with time windows and delivery robots (VRPTWDR). With the help of delivery robots, considerable operational time savings can be achieved by dispatching robots to serve nearby customers while a driver is also serving a customer. We provide a mathematical model for the VRPTWDR and investigate the challenges and benefits of using delivery robots as assistants for city logistics. A two-stage matheurisitic algorithm is developed to solve medium scale VRPTWDR instances. Finally, results of computational experiments demonstrate the value of self-driving delivery robots in urban areas by highlighting operational limitations on route planning.

Suggested Citation

  • Chen, Cheng & Demir, Emrah & Huang, Yuan & Qiu, Rongzu, 2021. "The adoption of self-driving delivery robots in last mile logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 146(C).
    • Handle: RePEc:eee:transe:v:146:y:2021:i:c:s1366554520308565
    DOI: 10.1016/j.tre.2020.102214
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    References listed on IDEAS

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    1. Boysen, Nils & Schwerdfeger, Stefan & Weidinger, Felix, 2018. "Scheduling last-mile deliveries with truck-based autonomous robots," European Journal of Operational Research, Elsevier, vol. 271(3), pages 1085-1099.
    2. Akeb, Hakim & Moncef, Btissam & Durand, Bruno, 2018. "Building a collaborative solution in dense urban city settings to enhance parcel delivery: An effective crowd model in Paris," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 119(C), pages 223-233.
    3. Bruno Durand & Hakim Akeb & Btissam Moncef, 2018. "Building a collaborative solution in dense urban city settings to enhance parcel delivery: An effective crowd model in Paris [L'élaboration d'une solution collaborative de livraisons urbaines en vue d'améliorer la distribution des colis : un modèl," Post-Print hal-01781155, HAL.
    4. Ghiami, Yousef & Demir, Emrah & Van Woensel, Tom & Christiansen, Marielle & Laporte, Gilbert, 2019. "A deteriorating inventory routing problem for an inland liquefied natural gas distribution network," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 45-67.
    5. Ann-Kathrin Rothenbächer & Michael Drexl & Stefan Irnich, 2018. "Branch-and-Price-and-Cut for the Truck-and-Trailer Routing Problem with Time Windows," Transportation Science, INFORMS, vol. 52(5), pages 1174-1190, October.
    6. Boysen, Nils & Schwerdfeger, Stefan & Weidinger, Felix, 2018. "Scheduling last-mile deliveries with truck-based autonomous robots," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 126189, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    7. Wang, Zheng & Sheu, Jiuh-Biing, 2019. "Vehicle routing problem with drones," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 350-364.
    8. David Pisinger & Stefan Ropke, 2019. "Large Neighborhood Search," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, edition 3, chapter 0, pages 99-127, Springer.
    9. Jeong, Ho Young & Song, Byung Duk & Lee, Seokcheon, 2019. "Truck-drone hybrid delivery routing: Payload-energy dependency and No-Fly zones," International Journal of Production Economics, Elsevier, vol. 214(C), pages 220-233.
    10. Guido Perboli & Roberto Tadei & Daniele Vigo, 2011. "The Two-Echelon Capacitated Vehicle Routing Problem: Models and Math-Based Heuristics," Transportation Science, INFORMS, vol. 45(3), pages 364-380, August.
    11. Villegas, Juan G. & Prins, Christian & Prodhon, Caroline & Medaglia, Andrés L. & Velasco, Nubia, 2013. "A matheuristic for the truck and trailer routing problem," European Journal of Operational Research, Elsevier, vol. 230(2), pages 231-244.
    12. Stefan Poikonen & Bruce Golden, 2020. "The Mothership and Drone Routing Problem," INFORMS Journal on Computing, INFORMS, vol. 32(2), pages 249-262, April.
    13. Marius M. Solomon, 1987. "Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints," Operations Research, INFORMS, vol. 35(2), pages 254-265, April.
    Full references (including those not matched with items on IDEAS)

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