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In recent times, there has been a notable increase in interest surrounding the integration of Un-manned Aerial Vehicle (UAV) technology and vehicle routing problems (VRP) for package delivery purposes. While existing studies have explored various types of package deliveries utilizing VRP, limited attention has been given to on-demand food delivery. This study aims to develop a VRP model that incorporates practical constraints such as payload capacity and maximum flying range, with the primary objective of minimizing travel distance in food delivery operations. A comparative analysis is conducted among three delivery methods, including UAV delivery, to determine the most effective approach and assess the feasibility of each method. Through a case study analysis focused on a pizza delivery service in Sri Lanka, it was observed that implementing VRP in a motorbike delivery system resulted in reduced travel distance, time, cost, and CO2 emissions compared to the existing delivery system. Furthermore, the utilization of UAVs in conjunction with VRP yielded even greater improvements across all parameters. Based on a comprehensive cost analysis considering long-term operations, the UAV-based delivery system was identified as the most cost-effective method, followed by the VRP-incorporated motorbike delivery method. Although the VRP-incorporated motorbike delivery system exhibited a slightly higher average time per route compared to the existing method, the total travel time required to complete all routes remained lower. Consequently, the study concludes that the VRP-incorporated motorbike delivery system outperforms the existing delivery method for food delivery, with the use of UAVs incorporating VRP identified as the optimal delivery method among the three alternatives. The findings contribute valuable insights to the optimization of food delivery logistics, emphasizing the potential of VRP and exploring the feasibility of UAVs for sustainable and efficient long-term delivery solutions.
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Tom
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85--105
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Bibliogr. 25 poz., fig., tab.
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- University of Moratuwa, Department of Transport Management & Logistics Engineering, Sri Lanka
- University of Moratuwa, Center for Supply Chain, Operations and Logistics Optimization, Sri Lanka
autor
- Aalborg University, Department of Materials and Production, Denmark
autor
- University of Moratuwa, Center for Supply Chain, Operations and Logistics Optimization, Sri Lanka
autor
- University of Moratuwa, Center for Supply Chain, Operations and Logistics Optimization, Sri Lanka
Bibliografia
- [1] Abdirad, M., Krishnan, K., & Gupta, D. (2020). A three-stage algorithm for the large scale dynamic vehicle routing problem with industry 4.0 approach. ArXiv, abs/ 2008.04355v3. https://doi.org/10.48550/arXiv.2008.11719
- [2] Benarbia, T., & Kyamakya, K. (2022). A literature review of drone-based package delivery logistics systems and their implementation feasibility. Sustainability, 14(1), 360. https://doi.org/10.3390/su14010360
- [3] Edel, D. (2020, April 7). UPS Partners With Wingcopter For Drone Deliveries. Intelligent Living. https://www.intelligentliving.co/ups-wingcopter-drone-deliveries/
- [4] Fernando, W. M., Thibbotuwawa, A., Perera, H. N., & Ratnayake, R. M. C. (2022a). Close-open mixed vehicle routing optimization model with multiple collecting centers to collect farmers’ perishable produce. 2022 International Conference for Advancement in Technology (ICONAT) (pp. 1–8). IEEE. https://doi.org/10.1109/ICONAT53423.2022.9725977
- [5] Fernando, W. M., Thibbotuwawa, A., Perera, H. N., & Ratnayake, R. M. C. (2022b). Applying a capacitated heterogeneous fleet vehicle routing problem with multiple depots model to optimize a retail chain distribution network. 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 588–592). IEEE. https://doi.org/10.1109/IEEM55944.2022.9989636
- [6] Fernando, W. M., Thibbotuwawa, A., Perera, H. N., Nielsen, P., & Kilic, D. K. (2024). An integrated vehicle routing model to optimize agricultural products distribution in retail chains. Cleaner Logistics and Supply Chain, 10, 100137. https://doi.org/10.1016/j.clscn.2023.100137
- [7] Figueres, C. (2015). Take urgent action to combat climate change and its impacts. UN Chronicle, 51(4), 30-31. https://doi.org/10.18356/0ab994c7-en
- [8] Ghelichi, Z., Gentili, M., & Mirchandani, P. B. (2021). Logistics for a fleet of drones for medical item delivery: A case study for Louisville, KY. Computers and Operations Research, 135, 105443. https://doi.org/10.1016/j.cor.2021.105443
- [9] Hwang, J., Kim, H., & Kim, W. (2019). Investigating motivated consumer innovativeness in the context of drone food delivery services. Journal of Hospitality and Tourism Management, 38, 102–110. https://doi.org/10.1016/j.jhtm.2019.01.004
- [10] Karney, C. F. F. (2022). geographiclib. https://geographiclib.sourceforge.io/html/python/
- [11] Leather, J. (2009). Rethinking Transport and Climate Change. ADB Sustainable Development Working Paper Series, 10. https://www.adb.org/sites/default/files/publication/28489/adb-wp10-rethinking-transport-climate-change.pdf
- [12] Lee, D. D. (2019, October 12). Dicing with death to deliver a meal: how South Korea’s appetite for ordering in endangers drivers. https://www.scmp.com/week-asia/economics/article/3032555/dicing-death-deliver-meal-how-south-koreas-appetite-ordering
- [13] Li, X., Tupayachi, J., Sharmin, A., & Martinez Ferguson, M. (2023). Drone-aided delivery methods, challenge, and the future: A methodological review. Drones, 7(3), 191. https://doi.org/10.3390/drones7030191
- [14] Mathew, A. O., Jha, A. N., Lingappa, A. K., & Sinha, P. (2021). Attitude towards drone food delivery services—role of innovativeness, perceived risk, and green image. Journal of Open Innovation: Technology, Market, and Complexity, 7(2), 144. https://doi.org/10.3390/joitmc7020144
- [15] Moshref-Javadi, M., & Winkenbach, M. (2021). Applications and research avenues for drone-based models in logistics: A classification and review. Expert Systems with Applications, 177, 114854. https://doi.org/10.1016/j.eswa.2021.114854
- [16] Pitney Bowes Inc. (2020, October 14). Pitney bowes parcel shipping index reports continued growth as global parcel volume exceeds 100 billion for first time ever. http://news.pb.com/article_display.cfm?article_id=5958
- [17] Sorooshian, S., Khademi Sharifabad, S., Parsaee, M., & Afshari, A. R. (2022). Toward a modern last-mile delivery: Consequences and obstacles of intelligent technology. Applied System Innovation, 5(4), 82. https://doi.org/10.3390/asi5040082
- [18] Sundar, K., & Rathinam, S. (2014). Algorithms for routing an unmanned aerial vehicle in the presence of refueling depots. IEEE Transactions on Automation Science and Engineering, 11(1), 287–294. https://doi.org/10.1109/TASE.2013.2279544
- [19] Thibbotuwawa, A., Bocewicz, G., Nielsen, P., & Banaszak, Z. (2020). Unmanned aerial vehicle routing problems: A literature review. Applied Sciences, 10(13), 4504. https://doi.org/10.3390/app10134504
- [20] Thibbotuwawa, A., Bocewicz, G., Nielsen, P., & Zbigniew, B. (2019). Planning deliveries with UAV routing under weather forecast and energy consumption constraints. IFAC-PapersOnLine, 52(13), 820–825. https://doi.org/10.1016/j.ifacol.2019.11.231
- [21] Thibbotuwawa, A., Nanayakkara, P. R., Fernando, W. M., Jayalath, M. M., Perera, H. N., & Nielsen, P. (2023). A reverse logistics network model for handling e-commerce returns. IFAC-PapersOnLine, 56(2), 138–143. https://doi.org/10.1016/j. ifacol.2023.10.1559
- [22] United Nations. (2021). Fact sheet climate change. Sustainable transport conference. https://www.un.org/sites/un2.un.org/files/media_gstc/FACT_SHEET_Climate_Change.pdf
- [23] Vichova, K., Veselik, P., Heinzova, R., & Dvoracek, R. (2021). Road transport and its impact on air pollution during the COVID-19 pandemic. Sustainability, 13(21), 11803. https://doi.org/10.3390/su132111803
- [24] Yadav, V., & Narasimhamurthy, A. (2018). A heuristics based approach for optimizing delivery schedule of an Unmanned Aerial Vehicle (Drone) based delivery system. 2017 9th International Conference on Advances in Pattern Recognition (ICAPR 2017) (pp. 398–403). IEEE. https://doi.org/10.1109/ICAPR.2017.8593145
- [25] Zhang, J., & Li, Y. (2023). Collaborative vehicle-drone distribution network optimization for perishable products in the epidemic situation. Computers and Operations Research, 149, 106039. https://doi.org/10.1016/j.cor.2022.106039
Typ dokumentu
Bibliografia
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