DOI: 10.17587/prin.17.270-279

Modeling and Optimizing the Characteristics of a Multi-Agent Fast Delivery System using OpenStreetMap and AnyLogic

D. A. Pavlov¹, Postgraduate Researcher, dima-pavlov@phystech.edu, A. S. Akopov, D. Sc. (Eng), Professor, RAS Professor, Chief Scientist², Professor^{1, 3}, akopovas@umail.ru
¹ Moscow Institute of Physics and Technology (MIPT, Phystech), Dolgoprudny, Moscow Region, 141700, Russian Federation
² FGBUN Central Economic and Mathematical Institute of the Russian Academy of Sciences, Moscow, 117418, Russian Federation
³ MIREA — Russian University of Technology, Moscow, 119454, Russian Federation

Corresponding author: Dmitry A. Pavlov, Postgraduate Researcher, Moscow Institute of Physics and Technology (MIPT, Phystech), Dolgoprudny, Moscow Region, 141700, Russian Federation, E-mail: dima-pavlov@phystech.edu

The paper presents a simulation model of a multi-agent fast home delivery system based on OpenStreetMap (OSM) data, implemented in the AnyLogic environment. The goal of the research is to create a simulation model that maximizes the profit of the delivery service, while ensuring that 90 % of orders are completed within 30 minutes. A model structure has been created that includes couriers, pickers, warehouses, and households in an urban setting. Computational experiments were conducted to analyze different resource allocation scenarios, using stochastic approximation methods to optimize the simulation. The results obtained confirm the usefulness of agent-based modeling and OSM data for the design and analysis of last-mile logistics systems.

Keywords: simulation modeling, multi-agent systems, last-mile logistics, optimization, AnyLogic, OpenStreetMap

pp. 270—279

For citation:
Pavlov D. A., Akopov A. S. Modeling and Optimizing the Characteristics of a Multi-Agent Fast Delivery System using OpenStreetMap and AnyLogic, Programmnaya Ingeneria, 2026, vol. 17, no. 5, pp. 270—279. DOI: 10.17587/prin.17.270-279. (in Russian).

References:

1. Yao R. Multiple optimization methods of automatic application in last-mile delivery system, Proceedings of the 2023 International Conference on Power, Electrical Engineering, Electronics and Control (PEEEC), Athens, Greece, 2023, pp. 291—294. DOI: 10.1109/PEEEC60561.2023.00062.
2. Filiopoulou E., Bardaki C., Boukouvalas D. et al. Last-mile delivery options: Exploring customer preferences and challenges, Proceedings of the 17th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP 2022), Corfu, Greece, 2022, pp. 1—6. DOI: 10.1109/SMAP56125.2022.9942122.
3. Konstantakopoulos G. D., Gayialis S. P., Kechagias E. P. Vehicle routing problem and related algorithms for logistics distribution: A literature review and classification, Operational Research International Journal, 2022, vol. 22, pp. 2033—2062. DOI: 10.1007/s12351-020-00600-7.
4. Holland J. H., Miller J. H. Artificial Adaptive Agents in Economic Theory, American Economic Review, 1991, vol. 81, no. 2, pp. 365—371.
5. Makarov V. L., Bakhtizin A. R., Epstein J. M. Agent-based modeling for a complex world. 2nd edition, revised, Moscow, Scientific publications department, GAUGN, 2022, 74 p. DOI: 10.18254/978­5-604-5843-4-7.
6. Makarov V. L., Bakhtizin A. R., Sushko E. D., Sushko G. B. A Design System for Scalable Agent-Based Models with Multi-Stage Interactions of Agents Forming Social Connections, Lobachevskii Journal of Mathematics, 2020, vol. 41, no. 8, pp. 1492—1501. DOI: 10.1134/S1995080220080107.
7. Makarov V. L., Bakhtizin A. R., Epstein J. M. Agent-based modeling for a complex world. Part 1, Economics and the Mathematical Methods, 2022, vol. 58, no. 1, pp. 5—26. DOI: 10.31857/S042473880018970-6.
8. Makarov V. L., Bakhtizin A. R., Epstein J. M. Agent-based modeling for a complex world. Part 2, Economics and the Mathematical Methods, 2022, vol. 58, no. 2, pp. 7—21. DOI: 10.31857/S042473880020009-8.
9. Makarov V. L., Bakhtizin A. R., Beklaryan G. L., Akopov A. S. Development of Software Framework for Large-Scale Agent-Based Modeling of Complex Social Systems, Programmnaya Ingeneria, 2019, vol. 10, no. 4, pp. 167—177. DOI: 10.17587/prin.10.167-177 (in Russian).
10. Borshchev A. The Big Book of Simulation Modeling: Multi-method Modeling with Anylogic 6. AnyLogic, America, 2013, 612 p.
11. Akopov A. S., Beklaryan L. A. Agent-Based Modelling of Interacting Unmanned Vehicles Dynamics with the FLAME GPU, Programmnaya Ingeneria, 2023, vol. 14, no. 3, pp. 110—122. DOI: 10.17587/prin.14.110-122 (in Russian).
12. Akopov A. S. MBHGA: A Matrix-Based Hybrid Genetic Algorithm for Solving an Agent-Based Model of Controlled Trade Interactions, IEEE Access, 2025, vol. 13, pp. 26843—26863, DOI: 10.1109/ACCESS.2025.3539460.
13. Issaoui Y., Khiat A., Haricha K. et al. An advanced system to enhance and optimize delivery operations in a smart logistics environment, IEEE Access, 2022, vol. 10, pp. 6175—6193. DOI: 10.1109/ACCESS.2022.3141311.
14. Sathish T. Profit maximization in reverse logistics based on disassembly scheduling using hybrid bee colony and bat optimization, Transactions of the Canadian Society for Mechanical Engineering, 2019, vol. 43, no. 4, pp. 551—559. DOI: 10.1139/tcsme-2019-0017.
15. Zhou C., Ma N., Cao X. et al. Classification and literature review on the integration of simulation and optimization in maritime logistics studies, IISE Transactions, 2021, vol. 53, no. 10, pp. 1157—1176. DOI: 10.1080/24725854.2020.1856981.
16. Xiong H. Research on cold chain logistics distribution route based on ant colony optimization algorithm, Discrete Dynamics in Nature and Society, 2021, article 6623563. DOI: 10.1155/2021/6623563.
17. Yang F., Tao F. A bi-objective optimization VRP model for cold chain logistics: Enhancing cost efficiency and customer satisfaction, IEEE Access, 2023, vol. 11, pp. 127043—127056. DOI: 10.1109/ACCESS.2023.3332145.
18. Yan Q., Zhang Q. The optimization of transportation costs in logistics enterprises with time-window constraints, Discrete Dynamics in Nature and Society, 2015, article 365367, 10 p. DOI: 10.1155/2015/365367.
19. Bridgelall R. Optimization Problems in Transportation and Logistics: A Practical Guide, Basel, MDPI, 2024, 236 p. DOI: 10.3390/books978-3-7258-0697-3.