CREATION PRINCIPLES OF INTELLIGENT AUTOMATED DELIVERY MANAGEMENT SYSTEMS AT THE RAILWAY

H. I. Kyrychenko

doi:10.15802/stp2017/99950

Authors

H. I. Kyrychenko State Economy and Technology University of Transport, Ukraine https://orcid.org/0000-0002-6883-1877

DOI:

https://doi.org/10.15802/stp2017/99950

Keywords:

intelligent management system, formalized knowledge, means of engineering, automated systems, railway opera-tions, railways terminology, goods delivery process

Abstract

Purpose. The paper is aimed to analyze the functioning of the existing railway informational system, as the data storage, consideration of the ways to changeover to intelligent management system, including the application of semantic approach. It is necessary to change the paradigm of automated traffic management; this determines changing the paradigm in railway operation management. The development and introduction of new Business-Intelligence-type system requires the formalized knowledge on railway transportation process to be implemented, including in the form of ontology as the model and means to formalize the knowledge. New knowledge requires using the terms and concepts to describe the new processes and management objects in railway operations. Methodology. Formalizing the knowledge one should abstract away from classical definitions such as marshaling or freight station, train station, division station, dead-end station, railway junction, railway hub etc. Unified Modeling Languages for engineering the automated systems use the definitions: the objects’ categories, the class diagrams, statechart diagrams, sequence diagrams, activity diagrams, component diagrams, link diagrams in properties etc. These concepts enable formalizing the knowledge, bringing to homogeneous representing and using them for modeling the management processes of goods delivery. Findings. The data on timing the events of goods delivery process, breakings of schedule are taken into account in set-theoretical model on scripting scenarios for goods delivery process. One of the concepts in the model on breaking of schedule (from the knowledge base of the system ASK VPUZ-E enables to take into account the influence of possible factors and the real railway operations conditions. Originality. The ways to formalize knowledge on railway transportation process as the major premise to form intelligent informational software for management system were analyzed. The set-theoretical models on goods delivery management for scripting goods delivery process’ scenarios have been developed in the paper, the examples of using the means of object-oriented modeling to construct algorithm for technological processes have been given as well. Practical value. The author suggests new terms, definitions and concepts describing the logistic business processes and enabling to changeover to formalizing the knowledge and to forming the conditions of intelligent management system of goods delivery on railways.

Author Biography

H. I. Kyrychenko, State Economy and Technology University of Transport

Dep. «Management of Transportation Process»,
Lukashevich St., 19, Kyiv, Ukraine, 03049,
tel. +38 (044) 452 12 02

References

Bashlaiev, V. K., Tseitlin, S. Y., & Velykodnyi, V. V. (2007). About the network of automated control system creating of Ukrainian freight transport. Science and Transport Progress, 17, 18-21.

Vernigora, R. V., Yelnikova, L. O. (2014). Perspektivy sozdaniya intellektualnoy sistemy podderzhki prinyatiya operativnykh resheniy po upravleniyu rabotoy poezdnykh lokomotivov na zheleznodorozhnom poligone. Abstracts of the International Conference «Modern Problems of Intelligent Transport Systems Development», January 27-31, 2014, Dnipropetrovsk. 23-24. Dnipropetrovsk, Ukraine: DNURT.

Hladun, A. Y., & Rohushyna, Y. V. (2016). Semantychni tekhnolohii: pryntsypy ta praktyky. Kyiv: TOV VD «ADEF-Ukrayna».

Gladun, A. Y., & Rogushina, Y. V. (2007). Formirovaniye tyezaurusa pryedmyetnoy oblasti kak sryedstva modyelirovaniya informatsionnykh potryebnostyey polzovatyelya pri poiskye v intyernyetye. Vestnik komp'iuternykh i informatsionnykh tekhnologii, l, 26-33.

Gorbova, O. V. (2016). The improving of methods in technical-service of railway station’s operation.(PhD thesis). Available from Dnipropetrovsk National University of Railway Transport named after Academician V. Lazaryan, Dnipropetrovsk.

Kyrychenko, H. I., & Ovcharenko, S. M. (2013). Rozrobka metodu kontrolno-chasovykh tochok dlia kontroliu hrafikiv dostavky vantazhu. Transport Problems, 10, 112-118.

Kozachenko, D. M., Vernyhora, R. V., & Horbova, O. V. (2014). Methods of data collection on the functioning of the railway stations. Transport Systems and Transportation Technologies, 8, 58-64.doi: 10.15802/tstt2014/38087

Lomotko, D.V., Butko, T. V., & Rozsokha, O. V. (2012). Analiz teoretychnykh pidkhodiv shchodo otsinky efektyvnosti roboty zaliznyts Ukrainy. Railway Transport of Ukraine, 2, 36-38.

Lomotko, D. V., & Butko, T. V. (2012). Metodolohiia formuvannia intelektualnoi transportnoi systemy na zaliznychnomu transporti. Research Bulletin SWorld, 2(1), 45-46.

Kyrychenko, H. І., Strelko, O. H., Berdnychenko, Y. A., Petrykovets, O. V., & Kyrychenko, O. A. (2016). Scenarios modeling of cargo movement in the supply chains. Transport Systems and Transportation Technologies, 12, 32-37. doi: 10.15802/tstt2016/85882

Pavlyshenko, B. M. (2011). Text data grouping on the base of semantic context model. Eastern-European Journal of Enterprise Technologies, 5, 2(53), 234-247.

Petrashevskyi, O. L., Danylevskyi, V. V., & Tsymbal, N. N. (2010). Adaptatsiya metodologii kontseptualno-logicheskogo otobrazheniya i proektnogo modelirovaniya transportnykh sistem k zadacham upravleniya proektami. Transport Problems, 7, 56-60.

Kyrychenko, A. I., Haba, V. V., Yvashchuk, S. Y., & Petrykovets, O. V. (2013). Sistemnyy tezaurus protsessov multimodalnoy dostavki gruzov. Transport Problems, 10, 186-196.

Tseitlin, S. Y., Podoliak, S. V., & Vasylyshyn, І. D. (2015). Peredumovy stvorennia analitychnoi systemy. Stvorennia tsentralizovanoi bazy danykh finansovo-ekonomichnykh system. Abstracts of the International Conference «Modern information and communication technologies on a transport, in industry and education», December 16-17, 2015, Dnipropetrovsk. 97. Dnipropetrovsk, Ukraine: DNURT.

Tseytlin, S. Y., Kovalenko, L. A., & Nikolenko, M. V. (2015). Sozdanie elektronnogo arkhiva uchetnykhi otchetnykh form dannykh v ASK VP UZ-Ye. Abstracts of the International Conference «Modern information and communication technologies on a transport, in industry and education», December 16-17, 2015,Dnipropetrovsk. 41. Dnipropetrovsk, Ukraine: DNURT.

Ghosh, S., Sharma, N., Benevenut, F., Ganguly, N., & Gummadi, K. (2012). Cognos: crowdsourcing search for topic experts in microblogs. Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, August 12-16, 2012, Portland, Oregon, USA. 575-590. doi:10.1145/2348283.2348361

Di Noia, T., Di Sciascio, E., & Donini, F. M. (2007). Semantic Matchmaking as Non-Monotonic Reasoning:A Description Logic Approach. Journal of Artificial Intelligence Research, 29, 269-307.

Fernandez-López, M., Gomez-Perez, A., & Juristo, N. (1997). Methontology: From Ontological Art Towards Ontological Engineering. Proceedings of the Spring Symposium on Ontological Engineering of AAAI. Stanford University, California, 33-40.

Vicente-López, E., De Campos, L. M., Fernández-Luna, J. M., & Huete, J. F. (2016). Use of textual and conceptual profiles for personalized retrieval of political documents. Knowledge-Based Systems, 112, 127-141. doi: 10.1016/j.knosys.2016.09.005

De Campos, L. M., Fernandez-Luna, J. M., Huete, J. F., & Vicente-Lopez, E. (2014). Using personalization to improve XML retrieval. Knowledge and Data Engineering, 26 (5), 1280-1292.

Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques (3rd ed.). Burlington: Morgan Kaufmann.