Comparison of optimization methods in simulation models of complex organizational and technical systems

The relevance of the study is due to the need to improve the effectiveness of management decisions in complex organizational and technical systems. The problem of this study is to choose the most appropriate optimization method for specific tasks of organizational systems. The purpose of the article is to compare modern methods of optimization of complex organizational and technical systems, in particular, in the model of the transport system. Special attention is paid to minimizing the target function, which takes into account such parameters as passenger traffic, passenger waiting time, vehicle loading and the impact on the traffic situation. The study analyzed suitable optimization methods and implemented software implementation of optimization approaches for the transport system in the Python programming language. The practical part allows evaluating the effectiveness of each method in terms of the results of the objective function, the adequacy of the selected model parameters and the execution time of the algorithm. The results showed that the methods of particle swarm and differential evolution provide the best minimization of the objective function with optimally selected parameters of the range of motion, speed and capacity of the vehicle, however, these optimization methods require a lot of time for calculations. The materials of the article are of practical value for specialists in the field of process optimization and transport planning, offering recommendations on the choice of optimization methods depending on the goals and conditions of the task.

1. Pospelov K.N., Burlutskaya Z.V., Gintciak A.M., Troshchenko K.D. Multiparametric Optimization of Complex System Management Scenarios Based on Simulation Models. International Journal of Technology. 2023;14(8):1748–1758. https://doi.org/10.14716/ijtech.v14i8.6832

2. Xin L., Xu P., Manyi G. Logistics Distribution Route Optimization Based on Genetic Algorithm. Computational Intelligence and Neuroscience. 2022;2022. https://doi.org/10.1155/2022/8468438

3. Rebentrost P., Lloyd S. Quantum Computational Finance: Quantum Algorithm for Portfolio Optimization. KI – Künstliche Intelligenz. 2024. https://doi.org/10.1007/s13218-024-00870-9

4. Beketov S.M., Pospelov K.N., Redko S.G. A Human Capital Simulation Model in Innovation Projects. Control Sciences. 2024;(3):16–25. https://doi.org/10.25728/cs.2024.3.2

5. Mencarelli L. et al. A review on superstructure optimization approaches in process system engineering. Computers & Chemical Engineering. 2020;136. https://doi.org/10.1016/j.compchemeng.2020.106808

6. Rodzin S.I., Kureichik V.V. Theoretical issues and modern problems concerning development of cognitive bioinspiral optimization algorithms (a survey). Kibernetika i programmirovanie = Cybernetics and Programming. 2017;(3):51–79. (In Russ.).

7. Dianov S.V., Kalashnikov K.N., Rigin V.A. Search for Ways of Optimal Spatial Placement of Healthcare Infrastructure Facilities: a Review of Methodological Tools. Problemy razvitiya territorii = Problems of Territory's Development. 2021;25(2):108–127. (In Russ.). https://doi.org/10.15838/ptd.2021.2.112.7

8. Kureychik V.V., Rodzin S.I. Bio-Heuristics Inspired by Fauna (Review). Informatsionnye tekhnologii = Information Technologies. 2023;29(11):559–573. (In Russ.). https://doi.org/10.17587/it.29.559-573

9. Chernyshev Yu.O., Kubil V.N. A review of dynamic vehicle routing problems. Programmnye produkty i sistemy = Software & Systems. 2020;(3):491–501. (In Russ.). https://doi.org/10.15827/0236-235X.131.491-501

10. He Z., Liu Q., Zhao P. Challenges of passenger and freight transportation in mega-city regions: A systematic literature review. Transportation Research Interdisciplinary Perspectives. 2022;16. https://doi.org/10.1016/j.trip.2022.100730

11. Chernyshev Yu.O., Kubil V.N., Trebukhin A.V. Overview of fuzzy vehicle routing problems. Advanced Engineering Research. 2020;20(3):325–331. https://doi.org/10.23947/2687-1653-2020-20-3-325-331

12. De La Torre R., Corlu C.G., Faulin J., Onggo B.S., Juan A.A. Simulation, Optimization, and Machine Learning in Sustainable Transportation Systems: Models and Applications. Sustainability. 2021;13(3). https://doi.org/10.3390/su13031551

13. Wu D., Lisser A. A deep learning approach for solving linear programming problems. Neurocomputing. 2023;520:15–24. https://doi.org/10.1016/j.neucom.2022.11.053

14. Sangeetha V., Vijayarangam J., Thirisangu K., Elumalai P. Simplex based solution for a fuzzy transportation problem. Malaya Journal of Matematik. 2021;S(1):393–396.

15. Alekhin R.A., Kubarkov Yu.P., Zakamov D.V., Umyarov D.V. Overview of metaheuristic optimization techniques applied to solving power engineering problems. Vestnik Samarskogo gosudarstvennogo tekhnicheskogo universiteta. Seriya: Tekhnicheskie nauki = Vestnik of Samara State Technical University. Technical Sciences Series. 2019;(3):6–19. (In Russ.).

16. Wei L., Zhang Z., Zhang D., Leung S.C.H. A simulated annealing algorithm for the capacitated vehicle routing problem with two-dimensional loading constraints. European Journal of Operational Research. 2018;265(3):843–859. https://doi.org/10.1016/j.ejor.2017.08.035

17. Lee J., Perkins D. A simulated annealing algorithm with a dual perturbation method for clustering. Pattern Recognition. 2021;112. https://doi.org/10.1016/j.patcog.2020.107713

18. Viazovychenko Y., Larin O. Stochastic Optimization Algorithms for Data Processing in Experimental Self-heating Process. In: Integrated Computer Technologies in Mechanical Engineering 2020 – Synergetic Engineering, 28–30 October 2020, Kharkiv, Ukraine. Cham: Springer; 2021. pp. 644–653. https://doi.org/10.1007/978-3-030-66717-7_55

19. Opara K.R., Arabas J. Differential Evolution: A survey of theoretical analyses. Swarm and Evolutionary Computation. 2019;44:546–558. https://doi.org/10.1016/j.swevo.2018.06.010

20. Patsey N.E., Ryabychina O.P. Appling of evolutionary algorithms for searching the optimal route. Problemy infokommunikatsii. 2020;(1-1):38–43. (In Russ.).

21. Gad A.G. Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review. Archives of Computational Methods in Engineering. 2022;29(5):2531–2561. https://doi.org/10.1007/s11831-021-09694-4

22. Yan F., Wang Y. Modeling and Solving the Vehicle Routing Problem with Multiple Fuzzy Time Windows. In: ICMSEM 2017: Proceedings of the Eleventh International Conference on Management Science and Engineering Management, 28–31 July 2017, Kanazawa, Japan. Cham: Springer; 2017. pp. 847–857. https://doi.org/10.1007/978-3-319-59280-0_69

23. Smolentseva T.E. Methods of determining objective function organizational systems. Modelirovanie, optimizatsiya i informatsionnye tekhnologii = Modeling, Optimization and Information Technology. 2018;6(3):143–152. (In Russ.). https://doi.org/10.26102/2310-6018/2018.22.3.011

24. Buylova M.V. Development of urban public transport route networks. Tekhniko-tekhnologicheskie problemy servisa. 2022;(1):45–52. (In Russ.).

25. Ermoshin N.A., Romanchikov S.A. Minimizatsiya avtotransportnykh izderzhek v usloviyakh neopredelennosti sostoyaniya dorozhnoi seti. In: Avtomobili, transportnye sistemy i protsessy: nastoyashchee, proshloe i budushchee: Sbornik statei 2-i Mezhdunarodnoi nauchno-tekhnicheskoi konferentsii, 22 May 2020, Kursk, Russia. Kursk: Southwest State University; 2020. pp. 119–122. (In Russ.).