EFFECTIVENESS OF OBJECT-ORIENTED PROGRAMMING METHODS AND PATTERNS FOR PLANNING RURAL RESCUE TEAMS
DOI:
https://doi.org/10.32718/agroengineering2025.29.208-217Keywords:
object-oriented programming, design patterns, efficiency, rural communities, rescue teams, information systems, optimizationAbstract
The article substantiates an approach to improving the effectiveness of software models designed for planning voluntary rescue teams in rural communities based on the integration of object-oriented programming methods and design patterns. It is shown that combining SOLID principles with the use of Strategy and Observer patterns significantly reduces architectural complexity and creates the conditions for developing scalable and flexible solutions that can adapt to changes in algorithms and external conditions. The software modules implemented in Python 3.12 allowed for an experimental assessment of the impact of programming approaches on key system quality indicators. The research was carried out using the example of tasks for planning the location of volunteer rescue teams for Sheptytskyi urban territorial community in Lviv region. The results prove that the use of classical procedural approaches is not effective enough, since such systems are characterized by low flexibility, significant maintenance costs, and poor code reuse capabilities. The transition to classic object-oriented programming (OOP) significantly improves performance, but the maximum effect is achieved by integrating OOP with patterns, which has increased the quality of implementation to 0.92, scalability to 0.89, component reuse to 0.91, and at the same time reduced the complexity of the architecture to 0.40. This confirms the feasibility of implementing patterns for building modern information systems in the field of security and planning rescue infrastructure in rural communities. The practical significance lies in the possibility of further integration of the developed modules into systems for spatial modeling and optimization of rescue formations. Research prospects include expanding the set of optimization algorithms, applying stochastic and robust methods, and using dynamic spatial data to form a comprehensive set of tools to support management decisions for communities.
References
Adasme, P., Viveros, A., & Dehghan Firoozabadi, A. (2024). Quadratic p-median problem: A Bender’s decomposition and a meta-heuristic local-based approach. Symmetry, 16 (9), 1114. https://doi.org/10.3390/sym16091114.
Alexander, D. (2015). Disaster and emergency planning for preparedness, response, and recovery. Oxford Research Encyclopedia of Natural Hazard Science. https://doi.org/10.1093/acrefore/9780199389407.013.12.
Almadi, S. H. S., Hooshyar, D., & Ahmad, R. B. (2021). Bad smells of Gang of Four design patterns: A decade systematic literature review. Sustainability, 13(18), 10256. https://doi.org/10.3390/su131810256.
Bafandeh Mayvan, B., Rasoolzadegan, A., & Ghavidel Yazdi, Z. (2016). The state of the art on design patterns: A systematic mapping of the literature. Journal of Systems and Software, 125, 93–118. https://doi.org/10.1016/j.jss.2016.11.030.
Bushong, V., Abdelfattah, A. S., Maruf, A. A., Das, D., Lehman, A., Jaroszewski, E. … Bures, M. (2021). On microservice analysis and architecture evolution: A systematic mapping study. Applied Sciences, 11(17), 7856. https://doi.org/10.3390/app11177856.
Chen, R., Qian, X., Miao, L., & Ukkusuri, S. V. (2020). Optimal charging facility location and capacity for electric vehicles considering route choice and charging time equilibrium. Computers & Operations Research, 113, 104776. https://doi.org/10.1016/j.cor.2019.104776.
Gamma, E., Helm, R., Johnson, R., & Vlissides, J. (1995). Design patterns: Elements of reusable object-oriented software. Addison-Wesley.
Hutsol, T., Mudryk, K., Francik, S., & Oliinyk, R. (2023). Intelligent tools for selecting configurations of renewable energy equipment for livestock farms. Energies, 16(22), 7605. https://doi.org/10.3390/en16227605.
Kondysiuk I., Tryhuba I., & Boiarchuk O. (2021). Intellectual information system for formation of portfolio projects of motor transport enterprises. CEUR Workshop Proceedings, 3109, 44–52. Retrieved from https://ceur-ws.org/Vol-3109/paper7.pdf. (Accessed February 09, 2025).
Lamontagne, S., Carvalho, M., & Atallah, R. (2024). Accelerated Benders decomposition and local branching for dynamic maximum covering location problems. Computers & Operations Research, 167, 106673. https://doi.org/10.1016/j.cor.2024.106673.
Lee, C., Kim, H., Chun, Y., & Griffith, D. A. (2024). Delineations for police patrolling on street network segments with p-median location models. ISPRS International Journal of Geo-Information, 13(11), 410. https://doi.org/10.3390/ijgi13110410.
Murray, A. T. (2025). Beyond location modeling and GIS: Integration and bridging. Computers & Operations Research, 180, 107073. https://doi.org/10.1016/j.cor.2025.107073.
Ratushnyi, A., Ptashnyk, V., Koval, L., Lub, P., & Tatomyr, A. (2023). A neural network model for predicting the duration of emergency response projects. In Proceedings of the IEEE 18th International Conference on Computer Science and Information Technologies (CSIT) (pp. 1–4). https://doi.org/10.1109/csit61576.2023.10324279.
Ratushnyi, R., Khmel, P., Martyn, E., & Prydatko, O. (2019). Substantiating the effectiveness of projects for the construction of dual systems of fire suppression. Eastern-European Journal of Enterprise Technologies, 4(3-100), 46–53.
Tryhuba, A. M., Koval, N. Ya., Ratushnyi, A. R., Tryhuba, I. L., & Shevchuk, V. V. (2023). Algorithm for the routes formation of food raw materials procurement on the community territory taking into account the production conditions during emergency situations. Applied Aspects of Information Technology, 6(1), 60–73. https://doi.org/10.15276/aait.06.2023.5.
Tryhuba, A. M., Ratushnyi, R. T., Ratushnyi, A. R., Koval, L. S., & Ivanusa, A. I. (2025). Optymizatsia bezpekovoi infrastruktury u prohramakh pisliavoiennoho vidnovlennia z vykorystanniam suchasnykh heoinformatsiinykh system. Visnyk Lvivskoho derzhavnoho universytetu bezpeky zhyttiediialnosti, 31, 196–212. https://doi.org/10.32447/20784643.31.2025.20.
Tryhuba, A., Malanchuk, O., Ratushnyi, A., Koval, L., & Andrushkiv, O. (2024). Value-based hybrid management of community safety projects: Intelligent models for optimal location of volunteer rescue formations. Sustainability, 16(12), 4821. https://doi.org/10.3390/su16124821.
Tryhuba, A., Ratushnyi, A., Lub, P., Rudynets, M., & Visyn, O. (2023). The value formation model of the project's implementation for the territorial rescue structures creation to a consequences elimination of a military emergency situations. In Proceedings of the 4th International Workshop IT Project Management (ITPM 2023) (pp. 59–70). Warsaw. Retrieved from https://ceur-ws.org/Vol-3453/paper6.pdf. (Accessed February 09, 2025).
Twigg, J., & Mosel, I. (2017). Emergent groups and spontaneous volunteers in urban disaster response. Environment and Urbanization, 29(2), 443–458. https://doi.org/10.1177/0956247817721413.
Yakovlev, S., Shekhovtsov, S., Kirichenko, L., Matsyi, O., Podzeha, D., & Chumachenko, D. (2025). Continuous maximum coverage location problem with arbitrary shape of service areas and regional demand. Symmetry, 17, 676.
