Route planning for hazardous materials transportation: Multicriteria decision making approach

  • Mohammad Noureddine Faculty of science and technology, University of Djelfa, Djelfa, Algeria
  • Milos Ristic University of defence in Belgrade, Department of logistics, Belgrade, Serbia
Keywords: Hazardous Materials Routing, FUCOM, TOPSIS, MABAC, Multi-criteria Decision-making.


Transport of hazardous material (THM) represents a complex area involving a large number of participants. The imperative of THM is minimization of risks in the entire process of transportation from the aspect of everyone involved in it, which is not an easy task at all. To achieve this, it is necessary in its early phase to carry out adequate evaluation and selection of an optimal transport route. In this paper, optimal route criteria for THM are selected using a new approach in the field of multi-criteria decision-making. Weight coefficients of these criteria were determined by applying the Full Consistency Method (FUCOM). Evaluation and selection of suppliers is determined by applying the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and the MABAC (Multi-attributive Border Approximation Area Comparison) methods. In order to establish the stability of models and validate the results obtained from the FUCOM-TOPSIS-MABAC model, a sensitivity analysis (of ten different scenarios) was performed. The sensitivity analysis implied changes of the weight coefficients criteria with respect to their original value. The proposed route model was tested on the real example of the transport Eurodiesel in Serbia.


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Bandyopadhyay, S., & Bhattacharya R. (2013). Finding optimum neighbor for routing based on multi-criteria, multi-agent and fuzzy approach. Journal of Intelligent Manufacturing, 26(1), 1–18.

Chang, C.H., Lin, J.J., Linc, J.H., & Chiang, M.C. (2010). Domestic open-end equity mutual fund performance evaluation using extended TOPSIS method with different distance approaches. Expert Systems with Applications, 37(6): 4642-4649.

Das, A., Mazumder, T.N., & Gupta, A.K. (2012). Pareto frontier analyses based decision making tool for transportation of hazardous waste. Journal of Hazardous Materials, 227-228, 341–52.

Dilgir, R., Zein, S., & Popoff, A. (2005). Hazardous goods route selection criteria. Annual Conference of the Transportation Association of Canada, Calgary – Alberta.

Erkut, E., & Verter V. (1998). Modeling of transport risk for hazardous materials. Operations Research, 46(5), 625-642.

Gigovic, LJ., Pamucar, D., Bozanic, D. & Ljubojevic, S. (2017). Application of the GIS-DANP-MABAC multi-criteria model for selecting the location of wind farms: A case study of Vojvodina, Serbia. Renewable Energy, 103, 501-521.

Huang B. (2006). GIS-Based Route Planning for Hazardous Material Transportation, Journal of Environmental Informatics, 8(1), 49-57.

Huang, B., & Fery, P. (2005). Aiding route decision for hazardous material transportation, TRB 2005 Annual Meeting.

Huang, B., Long Cheu, R., & SengLiew, Y. (2004). GIS and genetic algorithms for HAZMAT route planning with security considerations. International Journal of Geographical Information Science, 18(8), 769-787.

Jia, S.J., Yi, J., Yang, G.K., Du, B., & Zhu, J. (2013). A multi-objective optimisation algorithm for the hot rolling batch scheduling problem. International Journal of Production Research, 51(3), 667-681.

Law, V., & Rocchi, S. (2008). Hazardous goods route study, Final report city of Prince George.
Li, R., & Leung, Y. (2011). Multi-objective route planning for hazardous goods using compromise programming. Journal of Geographical Systems, 13(3), 249–271.

Long, C.R., & Liew, Y.S. (2003). GIS-AHP model for HAZMAT routing with security considerations. Intelligent Transportation Systems, 3, 1644–1649.

Milovanovic, B. (2012). Prilog razvoju metodologije za izbor trasa za kretanje vozila koja transportuju opasnu robu sa aspekta upravlјanja rizikom, Beograd, Saobracajni fakultet Univerziteta u Beogradu. (in Serbian).

Monprapussorn, S., Watts, D., & Banomyong, R. (2009). Sustainable hazardous materials route planning with environmental consideration. Asian Journal on Energy and Environment, 10(2), 122-132.

Oluwoye, J. (2000). Transportation of hazardous goods and the environment: a conceprual framework of the planning for classification procedure of hazardous e goods, South African Transport Conference, South Africa.

Pamucar, D., & Cirovic, G. (2015). The selection of transport and handling resources in logistics centres using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications, 42, 3016- 3028.

Pamucar, D., Ljubojevic, S., Kostadinovic, D., & Djorovic, B. (2016). Cost and Risk aggregation in multi-objective route planning for hazardous materials transportation - A neuro-fuzzy and artificial bee colony approach. Expert Systems with Applications, 65, 1-15.

Pamucar, D., Petrovic, I., & Cirovic, G. (2018). Modification of the Best-Worst and MABAC methods: A novel approach based on interval-valued fuzzy-rough number. Expert Systems with Applications, 91, 89-106.

Pamucar, D., Stevic, Z., & Sremac, S. (2018). A New Model for Determining Weight Coefficients of Criteria in MCDM Models: Full Consistency Method (FUCOM). Symmetry, 10(9), 393.

Peng, X., & Dai, J. (2016). Approaches to single-valued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function. Neural Computing and Applications, 29(10), 939–954.

Peng, X., & Yang, Y. (2016). Pythagorean Fuzzy Choquet Integral Based MABAC Method for Multiple Attribute Group Decision Making. International Journal of Intelligent Systems, 31(10), 989–1020.

Roy, J., Ranjan, A., Debnath, A., & Kar, S. (2016). An extended MABAC for multi-attribute decision making using trapezoidal interval type-2 fuzzy numbers. Artificial Intelligence, arXiv:1607.01254.

Samuel, C. (2007). Frequency analysis of hazardous material transportation incidents as a function of distance from origin to incident location, Iowa, Iowa State University.

Sattayaprasert, W., Taneerananon, P., Hanaoka, S., & Pradhananga, R. (2008). Creating a risk-based network for HAZMAT logistics by route prioritization with AHP. IATSS Research, 32(1), 74–87.

Song, W., Ming, X., Wu, Z., & Zhu, B. (2014). A rough TOPSIS approach for failure mode and effects analysis in uncertain environments. Quality and Reliability Engineering International, 30(4), 473-486.

Srdjevic, B., Srdjevic, Z., & Zoranovic, T. (2002). PROMETHEE, TOPSIS i SP u višekriterijumskom odlucivanju u polјoprivredi, Letopis naucnih radova, 26(1), 5 – 23. (in Serbian).

Stevic, Z., Tanackov, I., Vasiljevic, M., Novarlic, B., & Stojic, G. (2016). An integrated fuzzy AHP and TOPSIS model for supplier evaluation. Serbian Journal of Management, 11(1), 15-27.

Talarico, L. (2015). Secure Vehicle Routing: models and algorithms to increase ecutity and reduce costs in the cash-in-transit sector. Faculty of Applied Economics, Universitas, Antwerp.

Verma, M. (2011). Railroad transportation of hazardous goods : A conditional exposure approach to minimize transport risk. Transportation Research Part C: Emerging Technologies, 19(5), 790–802.

Wijeratne, A., Turnquist, M., & Mirchandani, P. (1993). Multiobjective routing of hazardous materials in stochastic networks. European Journal of Operation Research 65(1), 33-43.

Xue, Y.X., Youa, J.X., Laic, X.D., & Liu, H.C. (2016). An interval-valued intuitionistic fuzzy MABAC approach for material selection with incomplete weight information. Applied Soft Computing, 38, 703–713.

Yu, S., Wang, J., & Wang, J. (2016). An Interval Type-2 Fuzzy Likelihood-Based MABAC Approach and Its Application in Selecting Hotels on a Tourism Website. International Journal of Fuzzy Systems, 19(1), 47-61.

Zhang, Y., Zhang, Y., Li, Y., Liu, S., & Yang, J. (2017). A Study of Rural Logistics Center Location Based on Intuitionistic Fuzzy TOPSIS. Mathematical Problems in Engineering, Article ID 2323057,
How to Cite
Noureddine, M., & Ristic, M. (2019). Route planning for hazardous materials transportation: Multicriteria decision making approach. Decision Making: Applications in Management and Engineering, 2(1), 66-85. Retrieved from