DROP-OFF PREDICTION MODELS AND ROUTE OPTIMIZATION FOR COST-EFFECTIVE DELIVERY OPERATIONS

Authors

  • Bhageerath Bogi Independent Researcher, USA

DOI:

https://doi.org/10.36676/j.sust.sol.v1.i4.48

Keywords:

DROP-OFF PREDICTION MODELS, ROUTE OPTIMIZATION

Abstract

The role of integrating drop-off prediction models with route optimization approaches in improving the cost-efficacy of delivery processes is an area examined in this paper. The experimental results obtained from deep learning along with evolutionary algorithms in this paper point towards strong evidence that there are significant enhancements in fare and cost effective routes. As such advanced techniques, they might bring about a positive impact to the extent that the delivery of logistics may be made in an environmentally friendly and economically efficient manner.

References

Pitakaso, R., Sethanan, K. and Srijaroon, N., 2020. Modified differential evolution algorithms for multi-vehicle allocation and route optimization for employee transportation. Engineering Optimization, 52(7), pp.1225-1243. DOI: https://doi.org/10.1080/0305215X.2019.1640691

Reyes, D., Erera, A., Savelsbergh, M., Sahasrabudhe, S. and O’Neil, R., 2018. The meal delivery routing problem. Optimization Online, 6571, p.2018.

Mishra, S., Mehran, B. and Sahu, P.K., 2020. Assessment of delivery models for semi-flexible transit operation in low-demand conditions. Transport Policy, 99, pp.275-287. DOI: https://doi.org/10.1016/j.tranpol.2020.09.004

Rajendran, S. and Srinivas, S., 2020. Air taxi service for urban mobility A critical review of recent developments, future challenges, and opportunities. Transportation research part E logistics and transportation review, 143, p.102090. DOI: https://doi.org/10.1016/j.tre.2020.102090

Abdollahi, M., Khaleghi, T. and Yang, K., 2020. An integrated feature learning approach using deep learning for travel time prediction. Expert Systems with Applications, 139, p.112864. DOI: https://doi.org/10.1016/j.eswa.2019.112864

Gavalas, D., Konstantopoulos, C. and Pantziou, G., 2016. Design and management of vehicle-sharing systems a survey of algorithmic approaches. Smart cities and homes, pp.261-289. DOI: https://doi.org/10.1016/B978-0-12-803454-5.00013-4

Sun, Q., Chien, S., Hu, D., Chen, G. and Jiang, R.S., 2020. Optimizing multi-terminal customized bus service with mixed fleet. IEEE Access, 8, pp.156456-156469. DOI: https://doi.org/10.1109/ACCESS.2020.3018883

Rahimi, M., Amirgholy, M. and Gonzales, E.J., 2018. System modeling of demand responsive transportation services Evaluating cost efficiency of service and coordinated taxi usage. Transportation Research Part E Logistics and Transportation Review, 112, pp.66-83. DOI: https://doi.org/10.1016/j.tre.2018.02.005

Khalid, M., Cao, Y., Aslam, N., Raza, M., Moon, A. and Zhou, H., 2019. AVPark Reservation and cost optimization-based cyber-physical system for long-range autonomous valet parking (L-AVP). IEEE Access, 7, pp.114141-114153. DOI: https://doi.org/10.1109/ACCESS.2019.2930564

Abduljabbar, R., Dia, H., Liyanage, S. and Bagloee, S.A., 2019. Applications of artificial intelligence in transport An overview. Sustainability, 11(1), p.189. DOI: https://doi.org/10.3390/su11010189

Nguyen, T.D., Nguyen, T.D., Nguyen, V.D., Pham, X.Q. and Huh, E.N., 2018. Cost-effective resource sharing in an internet of vehicles-employed mobile edge computing environment. Symmetry, 10(11), p.594. DOI: https://doi.org/10.3390/sym10110594

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Published

24-12-2024

How to Cite

Bhageerath Bogi. (2024). DROP-OFF PREDICTION MODELS AND ROUTE OPTIMIZATION FOR COST-EFFECTIVE DELIVERY OPERATIONS. Journal of Sustainable Solutions, 1(4), 158–164. https://doi.org/10.36676/j.sust.sol.v1.i4.48

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Original Research Articles

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