Technical gazette, Vol. 22 No. 5, 2015.
Original scientific paper
https://doi.org/10.17559/TV-20140626104653
Modeling of fuzzy logic system for investment management in the railway infrastructure
Dragan Pamučar
orcid.org/0000-0001-8522-1942
; University of Defence, Department of Logistic, Pavla Jurisica Sturma 33, 11000 Beograd, Serbia
Predrag Atanasković
; University of Novi Sad, Faculty of Technical Science, Dositeja Obradovica 6, 21000 Novi Sad, Serbia
Milica Miličić
; University of Novi Sad, Faculty of Technical Science, Dositeja Obradovica 6, 21000 Novi Sad, Serbia
Abstract
The railway level crossings (RLCs), places where a railway line and road cross each other at the same level, are considered to be potentially dangerous points for all traffic participants. In general, level crossings may be fitted with automatic and/or mechanically-operated signaling/interlocking systems (RLCsAO) that allow passing of trains by lowering the barrier for the road users. In addition, there are also RLCs provided only with traffic signs and related inventory that have no barriers at all (RLCsNO). Protection of these level crossings by introducing the automatic signaling/interlocking system (AO) calls for significant investments considering the fact that equipment required for RLC modernization is very expensive, not to mention the great number of RLCsNO planned for improvement. Therefore it cannot be expected all level crossings without barriers (RLCsNO) to be upgraded at the same time so as traffic safety level can be properly increased. The method to be followed when choosing which RLC is to be provided with the adequate safety equipment depends on certain criteria relevant for making the proper investment decision. The paper herein deals with modeling of fuzzy logic-based approach that will offer adequate support to management when prioritizing RLCsNO to be provided with automatic signaling/interlocking system (AO). Seven (7) criteria that may affect the investment decision have been identified. The experience-based knowledge of managers (experts) was transferred into the fuzzy logic rule-based system to create the unique knowledge base to be used for making decision on investment priorities (list of RLC according to priorities). The output is a criterion function value that may be applied to any RLC analyzed. Based on the obtained value of the criterion function, RLCsNO are classified in line with investment priorities. The paper also shows a research that covered 88 RLCsNO planned for upgrading on the territory of the City of Belgrade out of which only 25 were nominated for investment due to limited financial resources.
Keywords
decision making; fuzzy logic system; investment decision; multiciriteria decision making
Hrčak ID:
147286
URI
Publication date:
22.10.2015.
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