Volume 15, no. 4Pages 109 - 114

Parallel Delivery Operations Modelling

D.S. Zavalishchin, K.K. Vakolyuk
Some delivery organization scheme is considered. The key point is the principle of routes parallelization using several carriers at the same time and these auxiliary carriers can be based on the main carrier. An example of such a delivery system is a van carrying several autonomous carriers, which in turn can carry out simultaneous so-called parallel deliveries. Delivery routes are determined based on the coordinates of customers, the determination of acceptable starting points for auxiliary carriers, the technical and energy limitations of the main and auxiliary carriers, and the minimization of the amount of time spent on delivery operations. The developed algorithm for solving the problem on routing of delivery using primary and secondary carriers allows to reduce delivery time and resources. The algorithm is implemented in Python using the libraries for processing and visualization of trajectories and other space-time data, packages for extracting, modelling, analyzing and visualizing street networks on the example of the Yekaterinburg city.
Full text
operations research; routing problem; traveling salesman problem; nearest neighbors algorithm; delivery service.
1. van Duin J.H.R., Vlot T.S., Tavasszy L.A., Duinkerken M.B., van Dijk B. Smart Method for Self-Organization in Last-Mile Parcel Delivery. Transportation Research Record, 2021, vol. 2675, no. 4, pp. 260-270.
2. Zhang J., Campbell J.F., Sweeney II D.C., Hupman A.C. Energy Consumption Models for Delivery Drones: A Comparison and Assessment. Transportation Research, 2021, Part D, vol. 90, article ID: 102668.
3. Zavalishchin D., Vakolyuk K. Algorithm for Parallel Parcels Delivery Service. 2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, 2021, pp. 0367-0370. DOI: 10.1109/USBEREIT51232.2021.9455068
4. Euchi J., Sadok A. Hybrid Genetic-Sweep Algorithm to Solve the Vehicle Routing Problem with Drones. Physical Communication, 1959, vol. 44, pp. 269-271.
5. Tamke F., Buscher U. A Branch-And-Cut Algorithm for the Vehicle Routing Problem with Drones. Transportation Research, 2021, Part B, vol. 144, pp. 174-203.
6. Chung S.H., Sah B., Lee J. Optimization for Drone and Drone-Truck Combined Operations: A Review of the State of the Art and Future Directions. Computers and Operations Research, 2020, no. 123, article ID: 105004.
7. Dijkstra E.W. A Note on Two Problems in Connexion with Graphs. Numerical Mathematics, vol. 1, issus 1, 1959, pp. 269-271.
8. Macrina G., Pugliese L.D.P., Guerriero F., Laporte G. Drone-Aided Routing: a Literature Review. Transportation Research, 2020, Part C, no. 120, article ID: 102762144.
9. Boeing G. OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks. Computers, Environment and Urban Systems, 2017, vol. 65, no. 9, pp. 126-139.
10. Hunter J.D. Matplotlib: a 2D Graphics Environment. Computing in Science and Engineering, 2007, vol. 9, no. 3, pp. 90-95.
11. Timofeeva G.A., Martynenko A.V. Analysis of Transport Network Development via Probabilistic Modelling. Proceedings of 14th International Conference Stability and Oscillations of Nonlinear Control Systems, 2018, article ID: 8408407.
12. Zavalishchin D.S., Timofeeva G.A. Dynamic Approach to Transportation Planning under Uncertainty. AIP Conference Proceedings, 2017, article ID: 070018.