Defining risks on road sections during the transport of dangerous goods in the Serbian army using the linear mathematical programming model

Keywords: DEA model, fuzzy logic system, dangerous goods, risk, route preference

Abstract


Introduction/purpose: The paper presents a model for the selection of a route for the transport of dangerous goods using DEA (Data Envelopment Analysis) models and fuzzy logic systems. The presented model is used to define the risk on road sections during the transport of dangerous goods as well as to select the optimal route for the realization of the transport task.

Methods: The model consists of two phases. The first phase includes the application of DEA models in which formed input and output models are connected in the output DEA final form which shows routes with a satisfactory level of traffic safety and at the same time eliminates routes with low traffic safety. The second phase involves the application of fuzzy logic systems, and as a way out of the fuzzy system, preference is given to one route. Route evaluation is based on six criteria, namely: route length, number of access points, AADT (annual average daily traffic), the number of traffic accidents with fatalities, the number of traffic accidents with the injured and the number of traffic accidents with material damage. When the values of the input criteria are entered, a calculation and evaluation is performed, and, as an exit from the fuzzy system, preference is given to one of the entered routes (the route with the lowest level of risk). The criteria used were defined on the basis of expert assessments.

Results: A user program that represents decision support in traffic service.

Conclusion: The user platform was created for the Matlab R2015a software package with the ability to be adapted to specific problems.

References

Amirteimoori, A. & Khoshandam, L. 2011. A Data Envelopment Analysis Approach to Supply Chain Efficiency. Advances in Decision Sciences, 2011(art.ID:608324). Available at: https://doi.org/10.1155/2011/608324.>

Baykasoglu, A. & Golcuk, I. 2020. Revising ranking accuracy within WASPAS method. Kybernetes, 49(3), pp.885-895. Available at: https://doi.org/10.1108/K-01-2019-0052.>

Božanić, D. & Pamučar, D. 2014. Making of fuzzy logic system rules base for decision making support by aggregation of weights of rules premises. Tehnika, 69(1), pp.129-138 (in Serbian). Available at: https://doi.org/10.5937/tehnika1401129B.

Bubbico, R., Di Cave, S. & Mazzarotta, B. 2004. Risk analysis for road and rail transport of hazardous materials: a simplified approach. Journal of Loss Prevention in the Process Industries, 17(6), pp.477-482. Available at: https://doi.org/10.1016/j.jlp.2004.08.010.>

Cassini, P. 1998. Road transportation of dangerous goods: quantitative risk assessment and route comparison. Journal of hazardous materials, 61(1-3), pp.133-138. Available at: https://doi.org/10.1016/S0304-3894(98)00117-4.>

Castillo, J.E.A. 2004. Route Optimization For Hazardous Materials Transport. Ph.D. thesis. Enschede, The Netherlands: International institute for geo-information science and earth observation [online]. Available at: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.58.7226&rep=rep1&type=pdf [Accessed: 20January 2022].

Cavaignac, L., Dumas, A.& Petiot, R. 2021. Third-party logistics efficiency: an innovative two-stage DEA analysis of the French market. International Journal of Logistics Research and Applications, 24(6), pp.581-604.

Cooper, W.W. 2014. Origin and Development of Data Envelopment Analysis: Challenges and Opportunities. Data Envelopment Analysis Journal, 1(1), pp.3-10. Available at: https://doi.org/10.1561/103.00000002.>

Dantzig, G.B. 1947. Maximization of a linear function of variables subject to linear inequalities. In: Koopmans, T.C. (Ed.) Activity Analysis of Production and Allocation, pp.339-347. New York-London: Wiley & Chapman-Hall.

Dawson, P., Dobson, S. & Gerrard, B. 2000. Estimating coach efficiency in professional team sports: Evidence from English Association Eootball. Scottish Journal of Political Economy, 47(4), pp.399-421. Available at: https://doi.org/10.1111/1467-9485.00170.>

Fabiano, B., Curro, F., Palazzi, E. & Pastorino, R. 2002. A framework for risk assessment and decision - making strategies in dangerous good transportation. Journal of Hazardous Materials, 93(1), pp.1-15. Available at: https://doi.org/10.1016/S0304-3894(02)00034-1.>

Farrell, M. 1957. The Measurement of Productive Efficiency. Journal of the Royal Statistical Society, Series A (General), 120(3), pp.253-290. Available at: https://doi.org/10.2307/2343100.>

Jeremić, V.M. 2012. Statistical model of efficiency based on Ivanovic distance. Ph.D. thesis. Belgrade: University of Belgrade, Faculty of Organizational Sciences (in Serbian) [online]. Available at: http://nardus.mpn.gov.rs/bitstream/handle/123456789/1780/Disertacija.pdf?sequ [Accessed: 20 January 2022].

Kantorovich, L.V. 1960. Mathematical Methods of Organizing And Planning Production. Management Science, 6(4), pp.363-505. Available at: https://doi.org/10.1287/mnsc.6.4.366.>

Karande, A., Krishna, A., Jayasurya, R., Gopan, G., Gopinath, M.V., Kumar, S., Anoop, K.P., Panicker, V.V. & Varaprasad, G. 2019. Performance Analysis of Storage Warehouses in a Food Grain Supply Chain using Data Envelopment Analysis. In: 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, March 29-30, pp.1-4. Available at: https://doi.org/10.1109/ICSCAN.2019.8878776.>

Lavell, A. 2000. An approach to concept and definition in risk management terminology and practice. Geneva: ERD-UNDP, pp.1015-1022.

Milovanović, B. 2012. Development of methodology for the selection of routes for transporting dangerous goods in terms od risc managemet. Ph.D. thesis. Belgrade: University of Belgrade, Faculty of Transport and Traffic Engeeniring (in Serbian). Available at: https://doi.org/10.2298/BG20120706MILOVANOVIC.>

Mitrović Simić, J., Stević, Ž., Zavadskas, E.K., Bogdanović, V., Subotić, M., & Mardani, A. 2020. A Novel CRITIC-Fuzzy FUCOM-DEA-Fuzzy MARCOS Model for Safety Evaluation of Road Sections Based on Geometric Parameters of Road. Symmetry, 12(12), art.number:2006. Available at: https://doi.org/10.3390/sym12122006.

Nenadić, D. 2019.  Ranking danerous sections of the road using MCDM model. Decision Making: Aplications in Management and Engineering, 2(1), pp.115-131 [online]. Available at: https://dmame.rabek.org/index.php/dmame/article/view/31> (in Serbian) [Accessed: 20 January 2022].

Ormsby, R.W. & Le, N.B. 1988. Societal risk curves for historical hazardous chemical transportation accidents, Preventing Major Chemical and Related Process Accidents, pp.133-147. Institution of Chemical Engineers, Great Britain.

Pamučar, D., Stević, Ž. & Sremac, S. 2018. A New Model for Determining Weight Coefficients of Criteria in MCDM Models: Full Consistency Method (FUCOM). Symmetry, 10(9), art.number:393. Available at: https://doi.org/10.3390/sym10090393>

Pamučar, D., Žižović, M., Biswas, S. & Božanić, D. 2021. A new logarithm methodology of additive weights (LMAW) for multi-criteria decision-making: Application in logistics. Facta Universitatis, series: Mechanical Engineering, 19(3), pp.361-380. Available at: https://doi.org/10.22190/FUME210214031P>

Srdjevic, B., Medeiros, Y., Srdjevic, Z. & Schaer, M. 2002. Evaluating Management Strategies in Paraguacu River Basin by Analytic Hierarchy Process. In: 1st International Congress on Environmental Modelling and Software, Lugano, Switzerland, pp.42-47, June 24-27 [online]. Available at: https://scholarsarchive.byu.edu/iemssconference/2002/all/38/> [Accessed: 20 January 2022].

Thomson, B.J. 1999. International co-operation in hazardous materials accident preventions. Journal of Loss Prevention in the Process Industries, 12(3), pp.217-225. Available at: https://doi.org/10.1016/S0950-4230(98)00052-7.>

Tian, N., Tang, S., Che, A. & Wu, P. 2020. Measuring regional transport sustainability using super-efficiency SBM-DEA with weighting preference. Journal of Cleaner Production, 242, art.number:118474. Available at: https://doi.org/10.1016/j.jclepro.2019.118474.>

Vesković, S., Blagojević, A., Kasalica, S., Gojić, A., & Allamani, A. 2020. The application of the fuzzy AHP and DEA for measuring the efficiency of freight transport railway undertakings. Operational Research in Engineering Sciences: Theory and Applications, 3(2), pp.1-23 [online]. Available at:https://www.oresta.rabek.org/index.php/oresta/article/view/52> [Accessed: 20 January 2022].

Zadeh, L.A. 1965. Fuzzy sets. Information and Control, 8(3), pp.338-353. Available at: https://doi.org/10.1016/S0019-9958(65)90241-X.>

Published
2022/10/14
Section
Original Scientific Papers