ADAPTATION OF LOGISTICS NETWORK STRUCTURES IN EMERGENCY SITUATIONS
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Abstract
The subject of research in the article is the process of adapting topological structures of micrologistics networks under the negative influence of the external environment. The goal of the work is increasing the efficiency of technologies for rapid adaptation of logistics networks (LN) to the negative impacts of emergency situations by developing a mathematical model of their multi-criteria optimization under conditions of incomplete uncertainty of input data. The following tasks are solved in the article: analysis of the current state of the optimization problem in adapting topological network structures to the negative impacts of emergency situations; development of problem statements for rapid adaptation of topological network structures to the negative impacts of emergency situations; formalization of indicators for evaluating options for adapting LN structures to the negative impacts of emergency situations; development of mathematical models of multi-criteria optimization problems of topological structures under conditions of incomplete uncertainty of input data; development of an algorithm for comparing network construction options for interval-based input data. The following methods are used: systems theory, utility theory, combinatorial optimization, operations research, interval and fuzzy mathematics. Results. To achieve the goal, the formulation of tasks for rapid adaptation and reengineering of topological structures of networks to the negative impacts of emergency situations was developed; the formalization of indicators for evaluating options for adapting topological structures of networks to the negative impacts of emergency situations was performed; mathematical models of optimization problems of topological structures under conditions of incomplete uncertainty of input data were developed according to the indicators of the given costs, efficiency and time of network adaptation. An algorithm for comparing network construction options according to local and generalized criteria for interval-based estimates was proposed. Conclusions. According to the results of the study, a solution to the scientific and practical problem of increasing the efficiency of technologies for rapid adaptation of networks to the negative effects of emergencies is proposed by developing a mathematical model of their multi-criteria optimization under conditions of interval certainty of input data. The results obtained expand the methodological principles of automation of processes supporting the adoption of multi-criteria design decisions when designing and adapting topological structures of micrologistics networks. They allow obtaining effective solutions to the problems of the fastest possible restoration of supply using the means that remained after the onset of an emergency and subsequent reengineering of the network construction option. The practical use of the results obtained will allow reducing the time for restoration of supply in the event of damage to elements and infrastructure of logistics networks, and by using the technology of selecting subsets of effective options with interval-specified characteristics, guaranteeing the quality of design solutions and providing their more complete assessments.
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References
Ismaуilov, V. and Karpenko, A. (2024), “Evaluation of an innovative logistics system based on the logistics efficiency index”, Management and Entrepreneurship: Trends of Development, no. 4(30), pp. 128–138, doi: https://doi.org/10.26661/2522-1566/2024-4/30-11
Surjandari, I., Rindrasari, R. and Dhini, A. (2023), “Evaluation of Efficiency in Logistics Company: An Analysis of Last-Mile Delivery”, EVERGREEN Joint Journal of Novel Carbon Resource Sciences & Green Asia Strategy, no. 10(02), pp. 649–657, doi: https://doi.org/10.5109/6792811
Lee, P.F., Lam, W.S. and Lam, W.H. (2023), “Performance Evaluation of the Efficiency of Logistics Companies with Data Envelopment Analysis Model”, Mathematics, vol. 11(3), doi: https://doi.org/10.3390/math11030718
Beskorovainyi, V. and Sudik, A. (2021), “Optimization of topological structures of centralized logistics networks in the process of reengineering”, Innovative Technologies and Scientific Solutions for Industries, no. 1(15), pp. 23–31, doi: https://doi.org/10.30837/ITSSI.2021.15.023
Beskorovainyi, V. and Podolyaka, K. (2015), “Development of systemological model of the problem of structural and topological reengineering of large scale monitoring systems”, Eastern-European Journal of Enterprise Technologies, no. 3(75), pp. 37-42, doi: https://doi.org/10.15587/1729-4061.2015.43471
Beskorovainyi, V., Kuropatenko, O. and Gobov, D. (2019), “Optimization of transportation routes in a closed logistics system”, Innovative Technologies and Scientific Solutions for Industries, no. 4 (10), pp. 24–32, doi: https://doi.org/10.30837/2522-9818.2019.10.024
Kuziak V. (2023), “Management of logistics processes in ukraine: problems and solutions under martial law”, Economy and Society, is. 55 doi: https://doi.org/10.32782/2524-0072/2023-55-13.
Wang, Y, Peng, S. and Xu, M. (2021), “Emergency logistics network design based on space–time resource configuration”, Knowledge-Based Systems, vol. 223, 107041, doi: https://doi.org/10.1016/j.knosys.2021.107041
Kirilyuk, I., and Sokur, A. (2024),”Organization of logistics processes of the enterprise in the conditions of war: problems and solutions. Economy and Society, is. 61, doi: https://doi.org/10.32782/2524-0072/2024-61-54
Yelizyeva, A., Artiukh, R. and Persiyanova, E. (2019), “Target and system aspects of the transport infrastructure development program”, Innovative Technologies and Scientific Solutions for Industries, no. 3(9), pp. 81-90, doi: https://doi.org/10.30837/2522-9818.2019.9.081.
Kuchuk, H., Mozhaiev, O., Kuchuk, N., Tiulieniev, S., Mozhaiev, M., Gnusov, Y., Tsuranov, M., Bykova, T., Klivets, S., and Kuleshov, A. (2024), “Devising a method for the virtual clustering of the Internet of Things edge environment”, Eastern-European Journal of Enterprise Technologies, vol. 1, no. 9 (127), pp. 60–71, doi: https://doi.org/10.15587/1729-4061.2024.298431
Morozov, О. (2024), “The Problem of Synthesis of Complex Systems: Multicriteria and Uncertainty of Information”. Scientific Bulletin of Kyiv Institute of The National Guard of Ukraine, no. 1, pp. 6–10. doi: https://doi.org/10.59226/2786-6920.1.2024.6-10
Morozov, O. (2023), “The methodology for solving the problem of synthesis of topological and functional structures of repair systems for weapons and military equipment”, Scientific Bulletin of Kyiv Institute of the National Guard of Ukraine, no. 1, pp. 6–10, doi: https://doi.org/10.59226/2786-6920.1.2023.6-10
Beskorovainyi, V.V., Petryshyn, L.B. and Shevchenko O.Yu. (2020), “Specific subset effective option in technology design decisions”, Applied Aspects of Information Technology, vol. 3, no.1, pp. 443–455. doi: https://doi.org/10.15276/aait.01.2020.6
Beskorovainyi, V. (2020), “Combined method of ranking options in project decision support systems”, Innovative Technologies and Scientific Solutions for Industries, no 4 (14), pp. 13–20, doi: https://doi.org/10.30837/ITSSI.2020.14.013
Akram, M. and Alcantud, J.C.R. (2023), Multi-criteria Decision Making Methods with Bipolar Fuzzy Sets, Springer Singapore, 214 p., doi: https://doi.org/10.1007/978-981-99-0569-0
Huang, Z., Yue, H. and He, Q. (2023), “Research on uncertain multiple attribute decision making based on improved possibility degree relation model and its application”, Engineering Applications of Artificial Intelligence, vol. 123, part B., 106349, doi: https://doi.org/10.1016/j.engappai.106349
Wen, X. (2023), “Weighted hesitant fuzzy soft set and its application in group decision making”, Granular Computing, doi: https://doi.org/10.1007/s41066-023-00387-w
Stewart, R.H., Palmer, T.S. and DuPont, B. (2021), “A survey of multi-objective optimization methods and their applications for nuclear scientists and engineers”, Progress in Nuclear Energy, vol. 138, doi: https://doi.org/10.1016/j.pnucene.2021.103830
Drobintsev, P., Voinov, N., Kotlyarova, L., Selin, I. and Aleksandrova, O. (2019), “Optimization of Technological Processes at Production Sites Based on Digital Modeling”, Advanced Manufacturing and Automation IX. IWAMA, vol. 634, doi: https://doi.org/10.1007/978-981-15-2341-0_75
Romanenkov, Y., Mukhin, V., Kosenko, V., Revenko, D., Lobach, O., Kosenko, N. and Yakovleva, A. (2024), “Criterion for Ranking Interval Alternatives in a Decision-Making Task”, International Journal of Modern Education and Computer Science (IJMECS), 16(2), pp. 72–82, doi: https://doi.org/10.5815/ijmecs.2024.02.06
Bezkorovainyi V., Kolesnyk L., Gopejenko V. and Kosenko V. (2024), “The method of ranking effective project solutions in conditions of incomplete certainty”, Advanced Information Systems, vol. 8, no 2, pp. 27–38, doi: https://doi.org/10.20998/2522-9052.2024.2.04
Iastremska, O. (2018), “Logistics at an enterprise: the peculiarities of procurement activities”, Innovative Technologies and Scientific Solutions for Industries, no. 3 (5), pp. 141–148, doi: https://doi.org/10.30837/2522-9818.2018.5.141
Kryvoruchko O. and Kryvenko L. (2024), “Main directions for improving logistics processes: reengineering, optimization, automation”, The bulletin of transport and industry economics, no. 86, pp. 113–124, doi: https://doi.org/10.18664/btie.86.310012
Ghahremani-Nahr, J., Nozari, H. & Szmelter-Jarosz, A. (2024), “Designing a humanitarian relief logistics network considering the cost of deprivation using a robust-fuzzy-probabilistic planning method”, Journal of International Humanitarian Action, vol. 9, article number 19, doi: https://doi.org/10.1186/s41018-024-00163-8
Pu, X. and Zhao, X. (2024), “Post-Earthquake Emergency Logistics Location-Routing Optimization Considering Vehicle Three-Dimensional Loading Constraints”, Symmetry, vol. 16(8), article number 1080, doi: https://doi.org/10.3390/sym16081080
Dotsenko, N., Chumachenko, I., Galkin, A., Kuchuk, H. and Chumachenko, D. (2023), “Modeling the Transformation of Configuration Management Processes in a Multi-Project Environment”, Sustainability (Switzerland), vol. 15(19), article number 14308, doi: https://doi.org/10.3390/su151914308
Beskorovainyi V. and Draz О. (2021), “Mathematical models of decision support in the problems of logistics networks optimization”, Innovative Technologies and Scientific Solutions for Industries, no. 4 (18), pp. 5–14. doi: https://doi.org/10.30837/ITSSI.2021.18.005
Beskorovainyi, V. and Podoliaka, K. (2015), “Development of the mathematical model of the problem of reengineering topological structures of large-scale monitoring systems”, Eastern-European Journal of Enterprise Technologies, no. 4(76), pp. 49–55, doi: https://doi.org/10.15587/1729-4061.2015.47865
Govindan, K., Fattahi, M. and Keyvanshokooh, E. (2017), “Supply chain network design under uncertainty: A comprehensive review and future research directions”, European Journal of Operational Research, vol. 263, pp. 108–141, doi: https://doi.org/10.1016/j.ejor.2017.04.009
Pellicer, P. C. and Valero, F. A. (2018), “Identification of Reverse Logistics Decision Types from Mathematical Models”, Journal of Industrial Engineering and Management, vol. 11, no 2, pp. 239–249, doi: https://doi.org/10.3926/jiem.2530
Saaty, T.L. (2016), “The Analytic Hierarchy and Analytic Network Processes for the Measurement of Intangible Criteria and for Decision-Making”, Multiple Criteria Decision Analysis. International Series in Operations Research & Management Science, vol. 233, Springer, New York, pp. 363–419, doi: https://doi.org/10.1007/978-1-4939-3094-4_10
Figueira, J., Mousseau, V. and Roy, B. (2016), “ELECTRE Methods”, Multiple Criteria Decision Analysis. International Series in Operations Research & Management Science, vol. 233, Springer, New York, pp. 155–185, doi: https://doi.org/10.1007/978-1-4939-3094-4_5
Brans, J.P. and De Smet, Y. (2016), “PROMETHEE Methods Multiple Criteria Decision Analysis”, International Series in Operations Research & Management Science, vol. 233, Springer, New York, pp. 187–219, doi: https://doi.org/10.1007/978-1-4939-3094-4_6
Papathanasiou, J. and Ploskas, N. (2018), “TOPSIS. Multiple Criteria Decision Aid”, Springer Optimization and Its Applications, Cham: Springer, vol. 136, pp. 1–30, doi: https://doi.org/10.1007/978-3-319-91648-4
Bezkorovainyi, V., Russkin, V. and Titov S. (2023), “Мathematical model of the problem of optimizing logistics networks under conditions of interval determination of input data”, Bulletin of Kharkov National Automobile and Highway University, vol. 1, no. 102, pp. 95–103, doi: https://doi.org/10.30977/BUL.2219-5548.2023.102.1.95
Kaliszewski, I., Kiczkowiak, T. and Miroforidis, J. (2016), “Mechanical design, Multiple Criteria Decision Making and Pareto optimality gap”, Engineering Computations, vol. 33(3), pp. 876–895, doi: https://doi.org/10.1108/EC-03-2014-0065
Liu, P. (2023), “Decision-making and Utility Theory”, AEMPS, vol. 26, pp. 313–321, doi: https://doi.org/10.54254/2754-1169/26/20230590
Beskorovainyi, V., Kolesnyk, L. and Russkin, V. (2022), “Decision making support under conditions of incomplete consistency of expert advantages”, Innovative integrated computer systems in strategic project management, Collective monograph edited by I. Linde, ISMA, Riga, pp. 16–26, doi: https://doi.org/10.30837/MMP.2022.016
Dubnitskiy, V., Kobylin, A., Kobylin, O., Kushneruk, Y. and Khodyrev, A. (2023), “Calculation of Harrington function (desirability function) values under interval determination of its arguments”, Advanced Information Systems, vol. 7, no. 1, pp. 71–81, doi: https://doi.org/10.20998/2522-9052.2023.1.12
Kosheleva, O., Kreinovich, V. and Pham, U. (2021), “Decision-making under interval uncertainty revisited”, Asian Journal of Economics and Banking, vol. 5 (1), pp. 79–85, doi: https://doi.org/10.1108/AJEB-07-2020-0030
Stefanini, L. and Arana-Jimenez, M. (2019), “Karush-Kuhn-Tucker conditions for interval and fuzzy optimization in several variables under total and directional generalized differentiability”, Fuzzy Sets and Systems, vol. 362, pp. 1–34, doi: https://doi.org/10.1016/j.fss.2018.04.009
Stefanini, L., Guerra, M.L. and Amicizia, B. (2019), “Interval Analysis and Calculus for Interval-Valued Functions of a Single Variable. Part I: Partial Orders, gH-Derivative”, Axioms, vol. 8, no. 4, doi: https://doi.org/10.3390/axioms8040113
Beskorovainyi, V., Kolesnyk, L. and Chinwi Mgbere, Dr. (2023), “Mathematical models for determining the Pareto front for building technological processes options under the conditions of interval presentation of local criteria”, Innovative Technologies and Scientific Solutions for Industries, no. 2 (24), pp. 16–26, doi: https://doi.org/10.30837/ITSSI.2023.24.016