MATHEMATICAL MODELS OF DECISION SUPPORT IN THE PROBLEMS OF LOGISTICS NETWORKS OPTIMIZATION
DOI:
https://doi.org/10.30837/ITSSI.2021.18.005Keywords:
logistics network, optimization, multi criteria evaluation, effective option, decision supportAbstract
The subject of research in the article is the process of decision support in the problems of logistics networks optimization. The goal of the work is to develop a set of mathematical models of logistics network optimization problems to increase the efficiency of decision support systems by coordinating the interaction between automatic and interactive procedures of computer-aided design systems. The following tasks are solved in the article: review and analysis of the current state of the problem of decision support in the problems of logistics networks optimization; decomposition of the problem of decision support for the optimization of logistics networks; development of a mathematical model of the general problem of network optimization in terms of economy, efficiency, reliability and survivability; development of a set of technological mathematical models for the correct reduction of many effective options for building logistics networks for the final choice, taking into account difficult to formalize factors, knowledge and experience of the decision maker (DM). The following methods are used: systems theory, utility theory, optimization and operations research. Results. Analysis of the current state of the problem of logistics networks optimization has established the existence of the problem of correct reduction of a subset of effective options for their construction for ranking, taking into account difficult to formalize factors, as well as knowledge and experience of DM. The decomposition of the problem into tasks is performed: definition of the principles of network construction; network structure selection; determination of the topology of network elements; choice of network operation technology; determination of parameters of elements and communications (means of cargo delivery); multi criteria evaluation and selection of the best option for building a network. A mathematical model of the general problem of network optimization in terms of economy, efficiency, reliability and survivability is proposed. To coordinate the interaction between automatic and interactive network optimization procedures, it is proposed to use a combined method of ranking options, which allows you to identify and correctly reduce the subset of effective options for ranking DM. To implement the method, mathematical models of problems of the procedure of ranking options in the technologies of project decision support have been developed, which allow to combine the advantages of the technologies of the ordinalistic and cardinalistic approaches. Conclusions. The developed set of mathematical models expands the methodological bases of automation of processes of support of multi criteria decisions on optimization of logistic networks, allows to carry out correct reduction of set of effective options of their construction for the final choice taking into account factors, knowledge and experience of DM. The practical use of the proposed models and procedures will reduce the time and capacity complexity of decision support technologies, and through the use of the proposed selection procedures - to improve their quality across a variety of functional and cost indicators.
References
Bowersox, D., Closs, D. (2016), Logistical Management: The integrated supply chain process, Trans. from Eng. 2nd ed. [Logistika: Integrirovannaya tsep' postavok, Per. s angl., 2-e izd.], Olimp-Biznes, Moscow, P. 640.
Govindan, K., Fattahi, M., Keyvanshokooh, E. (2017), "Supply chain network design under uncertainty: A comprehensive review and future research directions", European Journal of Operational Research, Vol. 263, Р. 108–141.
Pascual, C. P., Faustino, A. V. (2018), "Identification of Reverse Logistics Decision Types from Mathematical Models", Journal of Industrial Engineering and Management, No. 11 (2), P. 239–249.
Beskorovainyi, V., Kuropatenko, O., Gobov, D. (2019), "Optimization of transportation routes in a closed logistics system", Innovative Technologies and Scientific Solutions for Industries, No. 4 (10). P. 24–32. DOI: https://doi.org/10.30837/2522-9818.2019.10.024.
Beskorovainyi, V., 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). P. 23–31. DOI: https://doi.org/10.30837/ITSSI.2021.15.023.
Beskorovainyi, V. V. (2002), "Systemological analysis of the problem of structural synthesis of geographically distributed systems" ["Sistemologicheskiy analiz problemy strukturnogo sinteza territorial'no raspredelennykh sistem"], Automated control systems and automation devices, Issue 120, P. 29–37.
Petrov, K. E., Deineko, A. A., Chalaya, O. V., Panferova, I. Y. (2020), "Method of ranking options in the procedure of collective expert evaluation" ["Metod ranzhyrovanyya variantiv pry provedenyy protsedury kollektyvnoho ékspertnoho otsenyvanyya"], Radioelectronics, Informatics, Management, No. 2, P. 84–94.
Bernasconi, M., Choirat, C., Seri, R. (2014), "Empirical properties of group preference aggregation methods employed in AHP: Theory and evidence", European Journal of Operational Research, No. 232, P. 584–592.
Podolyaka, O. A., Podolyaka, A. N. (2015), "Application of ordinal normalization and scrambling of criteria for solving multicriteria problems" ["Prymenenye poryadkovoy normalyzatsyy y skremblyrovanyya kryteryev dlya reshenyya mnohokryteryalʹnykh zadach"], Automotive and Electronics. Modern technologies, No. 8, P. 60–69.
Ataei, M., Shahsavany, H., Mikaeil, R. (2013), "Monte Carlo Analytic Hierarchy Process (MAHP) approach to selection of optimum mining method", International Journal of Mining Science and Technology, No. 23, P. 573–578.
Zlaugotne, B., Zihare, L., Balode, L., Kalnbalkite, A., Khabdullin, A., Blumberga, D. (2020), "Multi-Criteria Decision Analysis Methods Comparison", Environmental and Climate Technologies, No. 24(1), Р. 454–471.
Bagočius, V., Zavadskas, E. K., Turskis, Z. (2014), "Multi-person selection of the best wind turbine based on
the multi-criteria integrated additive-multiplicative utility function", Journal of Civil Engineering and Management, No. 20, P. 590–599.
Baky, I. A. (2014), "Interactive TOPSIS algorithms for solving multi-level non-linear multi-objective decision-making problems", Applied Mathematical Modelling, No. 38, P. 1417–1433.
Iastremska, O. (2018). "Logistics at an enterprise: the peculiarities of procurement activities", Innovative Technologies and Scientific Solutions for Industries, No. 3 (5). P. 141–148. DOI: https://doi.org/10.30837/2522-9818.2018.5.141.
Beskorovainyi, V., Imangulova, Z., Petrova, A. (2012), "Optimization of the number and location of distribution centers of the transport and storage system" ["Optimizatsiya kolichestva i mestopolozheniya raspredelitel'nykh tsentrov transportno-skladskoy sistemy"], Eastern-European Journal of Enterprise Technologies, No. 5/3 (59), P. 24–28.
Prokopenkov, V., Kozhin, Y., Malykh, O. (2019), "Determination of the optimal ring route passing through a given set of points on the map" ["Opredelenie optimal'nogo kol'tsevogo marshruta, prokhodyashchego cherez zadannoe mnozhestvo punktov na karte"], Innovative technologies and scientific solutions for industries, No. 1 (7), P. 102–112.
Kosenko, V., Gopejenko, V., Persiyanova, E. (2019), "Models and applied information technology for supply logistics in the context of demand swings", Innovative Technologies and Scientific Solutions for Industries, No. 1 (7), P. 60–68. DOI: https://doi.org/10.30837/2522-9818.2019.7.059.
Beskorovainyi, V. (2020), "Combined method of ranking options in project decision support systems", Innovative Technologies and Scientific Solutions for Industries, No. 4 (14), Р. 13–20. DOI: https://doi.org/10.30837/ITSSI.2020.14.013.
Mikhalevich, V. S., Volkovich, V. L. (1982), Computational methods of research and design of complex systems [Vychislitel'nyye metody issledovaniya i proyektirovaniya slozhnykh sistem], Moscow: Nauka, P. 288.
Beskorovainyi, V., Petryshyn, L., Shevchenko, O. (2020), "Specific subset effective option in technology design decisions", Applied Aspects of Information Technology, Vol. 3, No. 1, P. 443–455.
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T. (2002), "A fast and elitist multiobjective genetic algorithm: NSGA-II", IEEE transactions on evolutionary computation, Vol. 6 (2), P. 182–197.
Shadura, O. (2019), "Modification of genetic algorithms based on the method of non-centered principal components and standard tests" ["Modyfikatsiya henetychnykh alhorytmiv na osnovi metodu netsetrovanykh holovnykh komponent ta standartni testy"], World Science, No. 4 (44), P. 4–11.
Ishizaka, A., Nemery, P. (2013), Multi-Criteria Decision Analysis, John Wiley & Sons.
Beskorovainyi, V., Іmanhulova, Z. (2017), "Тechnology of large-scale objects system optimization", ECONTECHMOD, Vol. 06, No. 4, Р. 3–8.
Моrozov, О. О. (2015), "Methods of synthesis of the system of maintenance and repair of weapons and military equipment" ["Metodyka syntezu systemy tekhnichnoho obsluhovuvannia ta remontu ozbroiennia i viiskovoi tekhniky"], Military-technical collection, No. 12, P. 87–90. DOI: https://10.26906/SUNZ.2018.5.09.
Beskorovainyi, V., Berezovskyi, G. (2017), "Estimating the properties of technological systems based on fuzzy sets", Innovative Technologies and Scientific Solutions for Industries", No. 1 (1), P. 14–20.
Beskorovainyi, V., Berezovskyi, H. (2017), "Іdentification of preferences in decision support systems", ECONTECHMOD, Vol. 06, No. 4, Р. 15–20.
Beskorovainyi, V. (2017), "Parametric synthesis of models for multi criterial estimation of technological systems", Innovative Technologies and Scientific Solutions for Industries, No. 2 (2), P. 5–11.Received 23.07.2021
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Vladimir Beskorovainyi
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Our journal abides by the Creative Commons copyright rights and permissions for open access journals.
Authors who publish with this journal agree to the following terms:
Authors hold the copyright without restrictions and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-commercial and non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
Authors are permitted and encouraged to post their published work online (e.g., in institutional repositories or on their website) as it can lead to productive exchanges, as well as earlier and greater citation of published work.
