Development of method of multifactor classification of transport and logistic processes

Arkadii Bosov, Nataliya Khalipova, Iryna Prohoniuk, Violetta Kuzmenko, Viktor Duhanets, Inna Shevchenko

Abstract


A method of classification of a set of objects and/or processes in transport and logistics systems on the basis of a multifactor analysis was proposed.

Combination of the methods of statistical and factor analysis, the systems hierarchy analysis method and modern methods of logistic analysis has enabled formation of a complex classification model. A three-stage algorithm was proposed for solving multifactor problems of classification in the fields of transport and logistics and the theory of organization of cargo transportation. At the first stage, the factor influence is investigated based on the chosen criterion. At the second stage, a comprehensive analytical indicator is created that characterizes the influence of factors on the objects under study and is based on the systemic hierarchy analysis method. The use of the hierarchy analysis method enables both use of factual data based on a thorough statistical analysis and involvement of experts in solving multifactorial decision-making problems. The use of modern logistic analysis methods in the third stage makes it possible to reasonably divide classification objects in the transport and logistics systems among various numbers of dynamic classes. At this stage, it is also possible to involve experts in the formation of boundaries of classes. The model was implemented using the Symbolic Computing Package in the Maple-7 and the Microsoft Excel environments.

Classification of Ukraine's trading partner countries by indicators of international turnover of certain groups of goods which can be transported in universal containers according to the Ukrainian foreign economic activity classifier (UFEAC) was made. Through simulation, the set of partner countries in foreign trade was divided into three classes. Classification of Ukraine’s partner countries by indicators of international turnover of certain groups of goods which can be transported in universal containers in accordance with the Ukraine’s foreign economic activity classifier (UFEAC) has allowed us to divide a set of partner countries in foreign trade into three classes. The classification results are in good agreement with the classic ABC analysis. Classification of countries by the criterion of specific value of goods flows has allowed us to divide the set of countries under study into two classes. This is explained by the fact that classification was made for a set of all partner countries in the first example and twenty countries with the largest turnover were previously selected in the second example.

Application of the proposed method of multifactor classification ensures dividing the objects of classification, in general, among different numbers of groups. At the same time, the boundaries of dynamic classes which are formed on the basis of a comprehensive assessment of the factor influence and depend on the concrete formulation of the problem and the criteria taken into account.

The proposed classification method can be used in transport, logistics, customs and brokerage enterprises, as it allows for planning and controlling supply of goods to varying degrees of detail and apply appropriate strategies.

Further development and improvement of this study is possible in the direction of using intelligent systems and algorithms to solve problems of multifactor analysis of transport and logistics systems.


Keywords


classification of transport and logistics processes; multifactor analysis; freight traffic; customs statistics; logistic analysis

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References


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GOST Style Citations


Mezhdunarodnye transportnye koridory Ukrainy: seti i modelirovanie. Vol. 1 / Bakaev A. A., Pirozhkov S. I., Revenko V. L. et. al. Kyiv: KUETT, 2003. 518 p.

Pyrozhkov S., Preiher D., Maliarchuk I. Problemy realizatsiyi tranzytnoho potentsialu Ukrainy u konteksti rozshyrennia YeS i formuvannia YeEP // Ekonomika Ukrainy. 2005. Issue 3. P. 4–19.

Pro zatverdzhennia Kontseptsiyi reformuvannia transportnoho sektoru ekonomiky. Verkhovna Rada Ukrainy, 2000. No. 1684. URL: http://zakon5.rada.gov.ua/laws/show/1684-2000-%D0%BF

Pro skhvalennia transportnoi stratehiyi Ukrainy na period do 2020 roku. Verkhovna Rada Ukrainy, 2010. No. 2174-r. URL: http://zakon0.rada.gov.ua/laws/show/2174-2010-%D1%80

Khalipova N. V. Analiz shliakhiv pidvyshchennia efektyvnosti konteinernykh perevezen v Ukraini // Visnyk Akademiyi mytnoi sluzhby Ukrainy. Seriya: Tekhnichni nauky. 2014. Issue 1. P. 104–115. URL: http://nbuv.gov.ua/UJRN/vamsutn_2014_1_16

Khalipova N. V., Lesnikova I. Yu. Modeliuvannia ta analiz konteinernykh perevezen v Ukraini // Visnyk Akademiyi mytnoi sluzhby Ukrainy. Seriya: Ekonomika. 2014. Issue 1. P. 149–160. URL: http://nbuv.gov.ua/UJRN/vamsue_2014_1_23

Kos S., Vukić L., Brčić D. Comparison of External Costs in Multimodal Container Transport Chain // PROMET – Traffic&Transportation. 2017. Vol. 29, Issue 2. P. 243. doi: 10.7307/ptt.v29i2.2183 

Liu D., Yang H. Joint slot allocation and dynamic pricing of container sea–rail multimodal transportation // Journal of Traffic and Transportation Engineering (English Edition). 2015. Vol. 2, Issue 3. P. 198–208. doi: 10.1016/j.jtte.2015.03.008 

Kengpol A., Tuammee S., Tuominen M. The development of a framework for route selection in multimodal transportation // The International Journal of Logistics Management. 2014. Vol. 25, Issue 3. P. 581–610. doi: 10.1108/ijlm-05-2013-0064 

Litman T. Introduction to multimodal transportation planning: principles and practices. Victoria Transport Policy Institute, 2017. 21 p. URL: http://www.vtpi.org/multimodal_planning.pdf

Khalipova N. V. Modeliuvannia vantazhopotokiv zovnishno-ekonomichnoi diyalnosti // Visnyk Akademiyi mytnoi sluzhby Ukrainy. Seriya: Tekhnichni nauky. 2013. Issue 2. P. 48–63. URL: http://nbuv.gov.ua/UJRN/vamsutn_2013_2_9

Zadorov V. B., Fedusenko E. V., Fedusenko A. O. Zastosuvannia metodiv bahatokryterialnoi optymizatsiyi do planuvannia vantazhnykh perevezen // Upravlinnia rozvytkom skladnykh system. 2010. Issue 2. P. 27–30.

Kozina K. H. Teoretyko-metodychni osnovy faktornoho analizu konkurentospromozhnosti mizhnarodnykh avtotransportnykh vantazhnykh perevezen Ukrainy // Naukovyi visnyk Khersonskoho derzhavnoho universytetu. Ser.: Ekonomichni nauky. 2014. Issue 6 (2). P. 203–206.

Novikov A. O., Novikova M. M. Modeling of financial and economic security of transport enterprises based on factor analysis // Science and Transport Progress. Bulletin of Dnipropetrovsk National University of Railway Transport. 2014. Issue 6. P. 42–49. doi: 10.15802/stp2014/32769 

Chernysh V. I. Metodyka otsinky informatsyinykh ryzykiv z vykorystanniam metodu analizu ierarkhiy // Radioelektronni i kompiuterni systemy. 2012. Issue 1. P. 46–50.

Danchuk V. D., Oliinyk R. V., Svatko V. V. Vyznachennia efektyvnykh zasobiv vantazhnykh perevezen v transportnykh zadachakh metodom analizu ierarkhiy // Visnyk Natsionalnoho transportnoho universytetu. 2011. Issue 22. P. 127–136.

Nogin V. D. Prinyatie resheniy pri mnogih kriteriyah: ucheb.-metod. pos. Sankt-Peterburg: YUTAS, 2007. 104 p.

Metody nehladkoi optymizatsiyi u spetsialnykh zadachakh klasyfikatsiyi / Stetsiuk P. I., Berezovskyi O. A., Zhurbenko M. H., Kropotov D. O. Kyiv, 2009. 28 p.

D'yakonov A. G. Metody resheniya zadach klassifikacii s kategorial'nymi priznakami // Prikladnaya matematika i informatika. Trudy fakul'teta Vychislitel'noy matematiki i kibernetiki MGU imeni M. V. Lomonosova. 2014. Issue 46. P. 103–127.

Bureeva N. N. Mnogomerniy statisticheskiy analiz s ispol'zovaniem PPP “STATISTICA”: Uchebno-metodicheskiy material po programme povysheniya kvalifikacii «Primenenie programmnyh sredstv v nauchnyh issledovaniyah i prepodavanii matematiki i mekhaniki». Nizhniy Novgorod, 2007. 112 p.

Terent'ev P. A. Klassifikacii i modeli logistiki vozvratnyh potokov // Logistika segodnya. 2010. Issue 04 (40). P. 242–251.

Bondarenko I. O. Formation of estimated conditions for life cycle of deformation work of the railway track // Science and Transport Progress. Bulletin of Dnipropetrovsk National University of Railway Transport. 2015. Issue 3 (57). P. 107–117. doi: 10.15802/stp2015/46064 

Butko T. V., Prokhorchenko A. V. Formuvannia systemy klasyfikatsiyi zaliznychnykh dilnyts i napriamkiv za vydamy perevezen i katehoriyamy infrastruktury dlia ekspluatatsiynoi diyalnosti // Visnyk ekonomiky transportu i promyslovosti. 2012. Issue 38. P. 68–69.

Prokip A. V., Dudiuk V. S., Kolisnyk R. B. Orhanizatsiyni ta ekoloho-ekonomichni zasady vykorystannia vidnovliuvalnykh enerhoresursiv: monohrafiya / A. V. Prokip (Ed.). Lviv: ZUKTs, 2015. 332 p.

Brenych Ya. V., Tymoshchuk P. V. Neiromerezhevi metody rozviazannia zadachi klasyfikatsiyi // Naukovyi visnyk NLTU Ukrainy. 2012. Issue 22.13. P. 343–349.

Qiong L., Jie Y., Jinfang Z. Application of Clustering Algorithm in Intelligent Transportation Data Analysis // Communications in Computer and Information Science. 2011. P. 467–473. doi: 10.1007/978-3-642-24097-3_70 

Decision of Multimodal Transportation Scheme Based on Swarm Intelligence / Lei K., Zhu X., Hou J., Huang W. // Mathematical Problems in Engineering. 2014. Vol. 2014. P. 1–10. doi: 10.1155/2014/932832 

Ramadhani T., Hertono G. F., Handari B. D. An Ant Colony Optimization algorithm for solving the fixed destination multi-depot multiple traveling salesman problem with non-random parameters // AIP Conference Proceedings. 2017. doi: 10.1063/1.4991227 

Xu Q., Mao J., Jin Z. Simulated Annealing-Based Ant Colony Algorithm for Tugboat Scheduling Optimization // Mathematical Problems in Engineering. 2012. Vol. 2012. P. 1–22. doi: 10.1155/2012/246978 

Paklin N. M. Algoritmy klasterizacii na sluzhbe Data Mining // BaseGroup Labs. Tekhnologii analiza dannyh. URL: https://basegroup.ru/community/articles/datamining

Afifi A., Eyzen S. Statisticheskiy analiz. Moscow: MIR, 1982. 488 p.

Mahalanobis P. C. On the Generalized Distance in Statistics // Proc. of the National Institute of Sciences of India. 1936. Vol. 2, Issue 1. P. 49–55.

Fisher R. A. The use of multiple measurements in taxonomic problems // Annals of Eugenics. 1936. Vol. 7, Issue 2. P. 179–188. doi: 10.1111/j.1469-1809.1936.tb02137.x 

Sira O. V., Katkova T. I. Neiromerezheva produktsiyna ekspertna systema diahnostyky stanu // Naukovi pratsi VNTU. 2009. Issue 2. P. 59–64.

Miachyn V. H., Kutsynska M. V. Neiromerezhevyi pidkhid do klasteryzatsiyi haluzei promyslovosti Ukrainy za dzherelamy finansuvannia innovatsiynoiyi aktyvnosti pidpryiemstv // Naukovyi visnyk Khersonskoho derzhavnoho universytetu. Seriya: Ekonomichni nauky. 2016. Issue 20. P. 64–68.

Tkachova A. V. Klasternyi analiz metalurhiynykh pidpryiemstv na osnovi vyrobnychykh, finansovo-ekonomichnykh ta lohistychnykh pokaznykiv diyalnosti // Visnyk Zaporizkoho natsionalnoho universytetu. 2012. Issue 1 (13). P. 37–44. URL: http://web.znu.edu.ua/herald/issues/2012/eco-1-2012/037-44.pdf

Karpenko O. O. Obgruntuvannia efektyvnosti klasteryzatsiyi transportno-lohistychnykh pidpryiemstv // Vodnyi transport. 2015. Issue 2. P. 126–133. URL: http://nbuv.gov.ua/UJRN/Vodt_2015_2_20

Ducret R., Lemarié B., Roset A. Cluster Analysis and Spatial Modeling for Urban Freight. Identifying Homogeneous Urban Zones Based on Urban Form and Logistics Characteristics // Transportation Research Procedia. 2016. Vol. 12. P. 301–313. doi: 10.1016/j.trpro.2016.02.067 

Kovalova O. V., Oliinyk R. V. Analiz rehionalnoi dorozhno-transportnoi merezhi metodom ierarkhichnoi klasteryzatsiyi // Avtomobilni dorohy i dorozhne budivnytstvo. 2011. Issue 82. P. 3–10.

Aloshynskyi Ye. S., Zambrybor H. H. Analiz peredumov formuvannia prykordonnykh transportno-lohistychnykh klasteriv dlia udoskonalennia mizhnarodnykh zaliznychnykh vantazhnykh perevezen // Zbirnyk naukovykh prats Ukrainskoi derzhavnoi akademiyi zaliznychnoho transportu. 2014. Issue 150. P. 11–17.

Öcalir-Akünal E. V., Erol S. Using Cluster Analysis to Define the Position of a Developing Country in Global Transportation Services Trade Environment // Gazi University Journal of Science. 2016. Vol. 29, Issue 4. P. 751767.

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DOI: https://doi.org/10.15587/1729-4061.2018.128679

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