Volume List  / Volume 12 (2)



DOI: 10.7708/ijtte2022.12(2).08

12 / 2 / 253-271 Pages


Ivana Nikolić - University of Belgrade – Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, 11010 Belgrade, Serbia -

Nataša Bojković - University of Belgrade – Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, 11010 Belgrade, Serbia -

Tanja Živojinović - University of Belgrade – Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, 11010 Belgrade, Serbia -

Momčilo Dobrodolac - University of Belgrade – Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, 11010 Belgrade, Serbia -


Transport policy represents a process of regulating and controlling the provision of transport services. In the past, the main emphasis was on the efficient transport connections and safety of drivers and other participants in transportation. These issues are still important and topical; however, due to the enormous increase of vehicles on the streets and harmful gas emissions, noise, congestions, and other negative effects of transportation, some other topics emerged that need to be considered in the design of sustainable development strategies, especially in big cities. This explanation leads to the conclusion that setting a transport policy represents a typical multi-criteria decision-making problem. There are usually certain alternative directions in the design of transport policy that should be assessed by more evaluation criteria, often opposed to each other. This is exactly the problem that is considered in this paper where three concepts of last-mile delivery of postal items are analyzed as the possible directions in the design of transport policy in cities. We applied three multi-criteria decision-making techniques: WASPAS, ARAS, and CoCoSo. The proposed methodology is tested and verified in a real-life case study considering the city of Niš. In the concrete case, the results showed that the best alternative in the design of last-mile delivery activities at the city level is the introduction of inner-city hubs.

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Ali, A.; Rashid, T. 2021. Best–worst method for robot selection, Soft Computing 25: 563–583. https://doi.org/10.1007/s00500-020-05169-z.


Ballantyne, E.; Lindholm, M.; Whiteing, A. 2013. A comparative study of urban freight transport planning: Addressing stakeholder needs, Journal of Transport Geography 32: 93–101. https://doi.org/10.1016/j.jtrangeo.2013.08.013.


Biswas, T.K.; Stević, Ž.; Chatterjee, P.; Yazdani, M. 2019. An integrated methodology for evaluation of electric vehicles under sustainable automotive environment, In: Chatterjee, P.; Yazdani, M.; Chakraborty, S.; Panchal, D.; Bhattacharyya, S. (Eds.) Advanced multi-criteria decision making for addressing complex sustainability issues, 41-62. https://doi.org/10.4018/978-1-5225-8579-4.ch003.


Blagojević, M.; Ralević, P.; Šarac D. 2020. An integrated approach to analysing the cost efficiency of postal networks, Utilities Policy 62: 101002. https://doi.org/10.1016/j.jup.2019.101002.


Chakraborty, S.; Zavadskas, E.K. 2014. Applications of WASPAS Method in Manufacturing Decision Making, Informatica 25(1): 1-20. https://doi.org/10.15388/Informatica.2014.01.


Dobrodolac, M.; Lazarević, D.; Švadlenka, L.; Živanović, M. 2016. A study on the competitive strategy of the universal postal service provider, Technology Analysis & Strategic Management 28(8): 935-949. https://doi.org/10.1080/09537325.2016.1180357.


Duleba, S.; Moslem, S.; Esztergár-Kiss, D. 2021. Estimating commuting modal split by using the Best-Worst Method, European Transport Research Review 13: 29. https://doi.org/10.1186/s12544-021-00489-z.


Ecer, F.; Pamučar, D. 2020. Sustainable supplier selection: A novel integrated fuzzy best worst method (F-BWM) and fuzzy CoCoSo with Bonferroni (CoCoSo'B) multi-criteria model, Journal of Cleaner Production 266: 121981. https://doi.org/10.1016/j.jclepro.2020.121981.


Ghorabaee, M.K.; Amiri, M.; Zavadskas, E.K.; Antucheviciene, J. 2017. Supplier evaluation and selection in fuzzy environments: a review of MADM approaches, Economic Research - Ekonomska Istraživanja 30(1): 1073–1118. https://doi.org/10.1080/1331677X.2017.1314828.


Gonzalez-Feliu, J.; Pronello, C.; Salanova Grau, J. M. 2018. Multi-stakeholder collaboration in urban transport: State-of-the-art and research opportunities, Transport 33: 1079–1094. https://doi.org/10.3846/transport.2018.6810.


Hysing, E. 2009. Greening Transport-Explaining Urban Transport Policy Change, Journal of Environmental Policy & Planning 11(3): 243-261. https://doi.org/10.1080/15239080903056417.


Ilbahar, E.; Kahraman C. 2018. Retail store performance measurement using a novel interval-valued Pythagorean fuzzy WASPAS method, Journal of Intelligent & Fuzzy Systems 35(3): 3835-3846. https://doi.org/10.3233/JIFS-18730.


Jayant, A.; Singh, S.; Garg, S. K. 2018. An Integrated Approach with MOORA, SWARA, and WASPAS Methods for Selection of 3PLSP. In Proceedings of the International Conference on Industrial Engineering and Operations Management, Paris, France, 2497-2509.


Jovčić, S.; Simić, V.; Průša, P.; Dobrodolac, M. 2020. Picture Fuzzy ARAS Method for Freight Distribution Concept Selection, Symmetry 12(7): 1062. https://doi.org/10.3390/sym12071062.


Kant, P.; Gupta, S. 2020. Sustainable Urban Freight Strategies for Jaipur City, India. In: Golinska-Dawson P., Tsai KM., Kosacka-Olejnik M. (eds) Smart and Sustainable Supply Chain and Logistics – Trends, Challenges, Methods and Best Practices. EcoProduction (Environmental Issues in Logistics and Manufacturing). Springer, Cham. https://doi.org/10.1007/978-3-030-61947-3_10.


Karabašević, D.; Stanujkić, D.; Urošević, S.; Maksimović, M. 2016. An approach to personnel selection based on Swara and Waspas methods, BizInfo Journal of Economics, Management, and Informatics 7(1): 1-11. https://doi.org/10.5937/bizinfo1601001K.


Kennedy, C.; Miller, E.; Shalaby, A.; Maclean H.; Coleman J. 2005. The Four Pillars of Sustainable Urban Transportation, Transport Reviews 25(4): 393-414. https://doi.org/10.1080/01441640500115835.


Keršulienė, V.; Turskis, Z. 2014. A hybrid linguistic fuzzy multiple criteria group selection of a chief accounting officer, Journal of Business Economics and Management 15(2): 232–252. https://doi.org/10.3846/16111699.2014.903201.


Lazarević, D.; Dobrodolac, M. 2020. Sustainability trends in the postal systems of last-mile delivery, Perner’s Contacts 15(1). https://doi.org/10.46585/pc.2020.1.1547.


Lazarević, D.; Švadlenka, L.; Radojičić, V.; Dobrodolac, M. 2020. New Express Delivery Service and Its Impact on CO2 Emissions, Sustainability 12: 456. https://doi.org/10.3390/su12020456.


Majumder, P.; Paul, A.; Saha, P.; Majumder, M.; Baidya, D.; Saha, D. 2022. Trapezoidal fuzzy BWM-TOPSIS approach and application on water resources, Environment, Development and Sustainability (in print). https://doi.org/10.1007/s10668-022-02126-8.


Memon, J.; Uddin, M.; Abd Rozan, M.Z. 2013. Green postal service framework to reduce CO2 emissions in postal service industry, International Journal of Global Warming 5(3): 255-269. https://doi.org/10.1504/IJGW.2013.055361.


Mesran, M.; Suginam, S.; Utomo, D. 2020. Implementation of AHP and WASPAS (Weighted Aggregated Sum Product Assessment) Methods in Ranking Teacher Performance, IJISTECH International Journal of Information System & Technology 3(2): 173-182.


Mic, P.; Antmen, Z.F. 2021. A Decision-Making Model Based on TOPSIS, WASPAS, and MULTIMOORA Methods for University Location Selection Problem, SAGE Open 11(3): 1-18. https://doi.org/10.1177/21582440211040115.


Moslem, S.; Campisi, T.; Szmelter-Jarosz, A.; Duleba, S.; Nahiduzzaman, K.M.; Tesoriere, G. 2020. Best–Worst Method for Modelling Mobility Choice after COVID-19: Evidence from Italy, Sustainability 12(17): 6824. https://doi.org/10.3390/su12176824.


Ortega, J.; Sarbast M.; János T.; Tamás P.; Palaguachi, J.; Paguay, M. 2020. Using Best Worst Method for Sustainable Park and Ride Facility Location, Sustainability 12(23): 10083. https://doi.org/10.3390/su122310083.


Pamučar, D.; Ecer, F.; Cirovic, G.; Arlasheedi, M.A. 2020. Application of Improved Best Worst Method (BWM) in Real-World Problems, Mathematics 8(8): 1342. https://doi.org/10.3390/math8081342.


Pamučar, D.; Pejčić Tarle, S.; Parezanović, T. 2018a. New hybrid multi-criteria decision-making DEMATEL-MAIRCA model: sustainable selection of a location for the development of multimodal logistics centre, Economic Research - Ekonomska Istraživanja 31(1): 1641-1665. https://doi.org/10.1080/1331677X.2018.1506706.


Pamučar, D.; Petrović, I; Ćirović, G. 2018b. Modification of the Best-Worst and MABAC methods: A novel approach based on interval-valued fuzzy-rough numbers, Expert Systems with Applications 91: 89-106. https://doi.org/10.1016/j.eswa.2017.08.042.


Popović, M. 2021. An MCDM approach for personnel selection using the CoCoSo method, Journal of Process Management and New Technologies 9(3-4): 78-88. https://doi.org/10.5937/jouproman2103078P.


Ralević, P.; Dobrodolac, M.; Marković, D. 2016. Using a nonparametric technique to measure the cost efficiency of postal delivery branches, Central European Journal of Operations Research 24(3): 637-657. https://doi.org/10.1007/s10100-014-0369-0.


Rezaei, J. 2015. Best-Worst Multi-Criteria Decision-Making Method, Omega 53: 49-57. http://dx.doi.org/10.1016/j.omega.2014.11.009.


Rodríguez-Gutiérrez, P.; Guerrero-Baena, M. D.; Luque-Vílchez, M.; Castilla-Polo, F. 2021. An approach to using the best-worst method for supporting sustainability reporting decision-making in SMEs, Journal of Environmental Planning and Management 64(14): 2618-2640. https://doi.org/10.1080/09640568.2021.1876003.


Savelsbergh, M.; Van Woensel, T. 2016. 50th Anniversary Invited Article—City Logistics: Challenges and Opportunities, Transportation Science 50(2): 579–590. https://doi.org/10.1287/trsc.2016.0675.


Simić, V.; Lazarević, D.; Dobrodolac, M. 2021. Picture fuzzy WASPAS method for selecting last-mile delivery mode: a case study of Belgrade, European Transport Research Review 13(43). https://doi.org/10.1186/s12544-021-00501-6.


Stanujkić, D. 2015. Extension of the ARAS method for decision-making problems with interval-valued triangular fuzzy numbers, Informatica 26(2): 335–355. https://doi.org/10.15388/Informatica.2015.51.


Ulutaş, A.; Karakuş, C.B.; Topal, A. 2020. Location Selection for Logistics Center with Fuzzy SWARA and CoCoSo Methods, Journal of Intelligent and Fuzzy Systems 38(4): 4693–4709. https://doi.org/10.3233/JIFS-191400.


Viu, M.; Alvarez-Palau, E. 2020. The Impact of E-Commerce-Related Last-Mile Logistics on Cities: A Systematic Literature Review, Sustainability 12: 6492. https://doi.org/10.3390/su12166492.


Xiao, Y.M.; Zhou, B.Y. 2020. Does the development of delivery industry increase the production of municipal solid waste?-An empirical study of China, Resources Conservation and Recycling 155: 104577. https://doi.org/10.1016/j.resconrec.2019.104577.


Yazdani, M.; Zarate, P.; Zavadskas, E.K.; Turskis, Z. 2019. A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems, Management Decision 57(9): 2501-2519. https://doi.org/10.1108/MD-05-2017-0458.


Yörükoğlu, M.; Aydın, S. 2020. Digital Library Evaluation by SWARA-WASPAS Method, International Journal of Industrial and Manufacturing Engineering 14(6): 444–447.


Zavadskas, E.K.; Turskis, Z. 2010. A new additive ratio assessment (ARAS) method in multi-criteria decision-making, Technological and Economic Development of Economy 16(2): 159–172. https://doi.org/10.3846/tede.2010.10.


Zavadskas, E. K.; Turskis, Z.; Antuchevičienė, J.; Zakarevičius, A. 2012. Optimization of weighted aggregated sum product assessment, Elektronika ir Elektrotechnika 122(6): 3–6. https://doi.org/10.5755/j01.eee.122.6.1810.


Zavadskas, E.K.; Turskis, Z.; Bagočius, V. 2015. Multi-criteria selection of a deep-water port in the Eastern Baltic Sea, Applied Soft Computing 26: 180–192. https://doi.org/10.1016/j.asoc.2014.09.019.