A crowdsourcing-based optimal route selection for drug delivery in low- and middle-income countries

Abstract

The timely delivery of life-saving products such as medicines from the pharmacy to the patient’s location requires availability of an adequate road infrastructure and reliable directions to the patient’s home. However, in many regions of low- and middle-income countries (LMIC), the road infrastructure is in poor state, and medicine delivery is affected by flooded roads, unsafe routes, congestion, traffic disruptions due to accidents, and lack of reliable navigation directions. Owing to the proliferation of smartphones and increasing mobile connectivity, these days the vehicle users rely heavily on routing software apps to select and follow shortest or fastest routes for reaching the destination and deliver life-saving medicinal products. However, routing software apps such as Google and Waze are not able to detect flooded roads or insecure and unsafe roads in various locations in LMIC countries, which causes disruption in drug delivery. Hence, this paper proposes a crowdsourcing-based approach to select optimal drug delivery routes with the objective to prevent drug delivery disruption and to guarantee the required delivery time-widow. The novelty of the proposed approach is that it determines optimal drug delivery routes based on real-time crowdsourced data and using communication services such as SMS. Furthermore, it overcomes the limitation of routing software apps. The tests conducted using the proposed approach show promised results with no drug delivery disruption. In the rainy season, 71% all selected drug delivery routes recommended by proposed system were optimal compared with 89% in the dry season. The similar tests using Google Maps are less successful, where in the rainy season only 11% and in the dry season 49% of the selected routes were found to be optimal.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

References

  1. 1.

    Agency ES (2001) Limitation of existing systems. Retrieved from http://www.esa.int/Our_Activities/Navigation/Limitations_of_existing_systems

  2. 2.

    Balasingam M (2017) Drones in medicine - the rise of the machines. Int J Clin Pract 71(9):2–5. https://doi.org/10.1111/ijcp.12989

    Article  Google Scholar 

  3. 3.

    Carey N (2016) UPS-backed Rwandan blood deliveries show drones’ promise, hurdles, Reuters

  4. 4.

    Chatzimilioudis G, Konstantinidis A, Laoudias C, Zeinalipour-yazti D (2012) Crowdsourcing with smartphones. IEEE Int Comput 16:1–7. https://doi.org/10.1109/MIC.2012.70

    Article  Google Scholar 

  5. 5.

    Dalmeijer K, Spliet R (2018) A branch-and-cut algorithm for the time window assignment vehicle routing problem. Comput Oper Res 89:140–152. https://doi.org/10.1016/j.cor.2017.08.015

    MathSciNet  Article  MATH  Google Scholar 

  6. 6.

    Dorigo M, Stützle T (2019) Ant colony optimization: overview and recent advances. In handbook of metaheuristics (pp. 311–351). Springer, Cham

  7. 7.

    Edoh T (2017) Smart medicine transportation and medication monitoring system in EPharmacyNet. In Xplore IEEE (Ed.), 2017 International Rural and Elderly Health Informatics Conference (IREHI) (pp. 1–9). https://doi.org/10.1109/IREEHI.2017.8350381

  8. 8.

    Edoh TO, Teege G (2011) Using information technology for an improved pharmaceutical care delivery in developing countries. Study case: Benin. J Med Syst 35(5):1123–1134. https://doi.org/10.1007/s10916-011-9717-y

    Article  Google Scholar 

  9. 9.

    Edoh TO, Pawar PA, Loko LY (2018) Challenges facing health service delivery in developing countries and solution approaches: the case of Benin, a West-African developing country. In Handbook of Research on Emerging Perspectives on Healthcare Information Systems and Informatics (pp. 515-559). IGI global

  10. 10.

    Honda Corporation (n.d.) System Limitations. Retrieved February 24, 2019, from http://techinfo.honda.com/rjanisis/pubs/OM/RL0000/RL0000N00066B.pdf

  11. 11.

    Hong I, Kuby M, Murray AT (2018) A range-restricted recharging station coverage model for drone delivery service planning. Transp Res C 90(November 2016):198–212. https://doi.org/10.1016/j.trc.2018.02.017

    Article  Google Scholar 

  12. 12.

    Januszewski J (2013) Satellite navigation systems applications , the main utilization limits for maritime users. 36(108), 70–75

  13. 13.

    Kim S, Moon I (2019) Traveling salesman problem with a drone station. IEEE Trans Syst Man Cybern Syst Hum 49(1):42–52. https://doi.org/10.1109/TSMC.2018.2867496

    Article  Google Scholar 

  14. 14.

    Kumar PM, Devi G U, Manogaran G, Sundarasekar R, Chilamkurti N, Varatharajan R (2018) Ant colony optimization algorithm with internet of vehicles for intelligent traffic control system. Comput Netw 144:154–162. https://doi.org/10.1016/j.comnet.2018.07.001

    Article  Google Scholar 

  15. 15.

    Liu J, Shen H, Zhang X (2016) A survey of mobile crowdsensing techniques: a critical component for the internet of things. 2016 25th international conference on computer communication and networks (ICCCN) Waikoloa, HI, USA https://doi.org/10.1109/ICCCN.2016.7568484

  16. 16.

    Loidl M, Hochmair H (2018) Do online bicycle routing portals adequately address prevalent safety concerns? Safety 4(1):9. https://doi.org/10.3390/safety4010009

    Article  Google Scholar 

  17. 17.

    Milner G (2016) Pinpoint: how GPS is changing technology, culture, and our minds (1. edition). W. W. Norton & Company

  18. 18.

    Mzee PK, Chen Y (2012) Road safety in developing countries - the case of transportation management in Dar Es Salaam City. Appl Mech Mater 178–181:1806–1814

    Article  Google Scholar 

  19. 19.

    Neves-Moreira F, Almada-Lobo B, Cordeau JF, Guimarães L, Jans R (2018a) Solving a large multi-product production-routing problem with delivery time windows. Omega (United Kingdom), 163–183. https://doi.org/10.1016/j.omega.2018.07.006

  20. 20.

    Neves-Moreira F, Pereira da Silva D, Guimarães L, Amorim P, Almada-Lobo B (2018b) The time window assignment vehicle routing problem with product dependent deliveries. Transport Res E-Log 116(March):163–183. https://doi.org/10.1016/j.tre.2018.03.004

    Article  Google Scholar 

  21. 21.

    Phiboonbanakit Thananut TS (2018) Knowledge-based learning for solving vehicle routing problem. 1103–1111. https://doi.org/10.1145/3267305.3274166

  22. 22.

    Scott J, Scott C (2017) Drone delivery models for healthcare. 3297–3304. https://doi.org/10.24251/HICSS.2017.399

  23. 23.

    Shrivastava D, Agrawal A (2014) Traffic congestion detection in vehicular adhoc networks using GPS. In 2014 IEEE international conference on computational intelligence and computing research (pp. 1-7). IEEE

  24. 24.

    Sun G, Zhang Y, Liao D, Yu H, Du X, Guizani M (2018) Bus-trajectory-based street-centric routing for message delivery in urban vehicular ad hoc networks. IEEE Trans Veh Technol 67(8):7550–7563. https://doi.org/10.1109/TVT.2018.2828651

    Article  Google Scholar 

  25. 25.

    Taylor C (2018) Drones set to deliver blood and medical supplies to Ghana’s hospitals. Retrieved February 16, 2019, from https://www.cnbc.com/2018/12/13/drones-set-to-deliver-blood-and-medical-supplies-to-ghanas-hospitals.html

  26. 26.

    Troia S, Rodriguez A, Martin I, Hernandez JA, De Dios OG, Alvizu R, … Maier G (2018) Machine-learning-assisted routing in SDN-based optical networks. 2018 European Conference on Optical Communication (ECOC), (2), 1–3. https://doi.org/10.1109/ECOC.2018.8535437

  27. 27.

    Vattapparamban E, Güvenç I, Yurekli AI, Akkaya K, Uluaǧaç S (2016) Drones for smart cities: issues in cybersecurity, privacy, and public safety. 2016 International Wireless Communications and Mobile Computing Conference, IWCMC 2016, 216–221. https://doi.org/10.1109/IWCMC.2016.7577060

  28. 28.

    Walther L, Shetty S, Rizvanolli A, Jahn C (2018) Comparing two optimization approaches for ship weather routing. Oper Res Proc 2016:337–342. https://doi.org/10.1007/978-3-319-55702-1_45

    Article  Google Scholar 

  29. 29.

    Wayumba R, Mwangi P, Chege P (2017) Application of unmanned aerial vehicles in improving land registration in Kenya. Int J Res Eng Sci 5(5):05–11

    Google Scholar 

Download references

Acknowledgements

We acknowledge previous help of Mrs. SOSSOU Dolorès Valérie from University of Abomey Calvi for assisting us during the testing of proposed NoMap routing system.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Pravin Amrut Pawar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(PDF 183 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Edoh, T.O.C., Pawar, P.A. A crowdsourcing-based optimal route selection for drug delivery in low- and middle-income countries. Pers Ubiquit Comput (2020). https://doi.org/10.1007/s00779-020-01424-0

Download citation

Keywords

  • Optimal and alternative routing selection
  • Safety-related routing
  • Risk of the path
  • Internet of Things (IoT)
  • EPharmacyNet