Let's Connect
Follow Us
Watch Us
(+385) 1 2380 262
journal.prometfpz.unizg.hr
Promet - Traffic&Transportation journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

Login Submit

PDF
PUBLISHED
30.09.2022
DOI
https://doi.org/10.7307/ptt.v34i5.4122
ISSUE
2022 (Vol 34), Issue 5
LICENSE
Copyright (c) 2022 Snežana Tadić, Mladen Krstić, Milovan Kovač, Nikolina Brnjac
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Evaluation of Smart City Logistics Solutions

Authors:Snežana Tadić, Mladen Krstić, Milovan Kovač, Nikolina Brnjac

Abstract

The negative effects of goods flows realisation are most visible in urban areas as the places of the greatest concentration of economic and social activities. The main goals of this article were to identify the applicable Industry 4.0 technologies for performing various city logistics (CL) operations, establish smart sustainable CL solutions (SSCL) and rank them in order to identify those which will serve as the base points for future plans and strategies for the development of smart cities. This kind of problem requires involvement of multiple stakeholders with their opposing goals and interests, and thus multiple criteria. For solving it, this article proposed a novel hybrid multi-criteria decision-making (MCDM) model, based on BWM (Best-Worst Method) and CODAS (COmbinative Distance-based ASsessment) methods in grey environment. The results of the model application imply that the potentially best SSCL solution is based on the combination of the concepts of micro-consolidation centres and autonomous vehicles with the support of artificial intelligence and Internet of Things technologies. The main contributions of the article are the definition of original SSCLs, the creation of a framework and definition of criteria for their evaluation and the development of a novel hybrid MCDM model.

Keywords:city logistics, smart city, Industry 4.0, grey BWM, grey CODAS

References

  1. [1] European Commission. The European Green Deal. Brussels, 2019.
  2. [2] Tadić S, Zečević S. Global trends and their impact on city logistics management. Tehnika. 2016;71: 459–464. doi: 10.5937/tehnikal603459T.
  3. [3] Macharis C, Kin B. The 4 A’s of sustainable city distribution: Innovative solutions and challenges ahead. International Journal of Sustainable Transportation. 2017;11: 59–71. doi: 10.1080/15568318.2016.1196404.
  4. [4] Tadić S, Krstić M, Kovač M. Assessment of city logistics initiative categories sustainability. Environment, Development and Sustainability. 2022: 1–37. doi: 10.1007/s10668-021-02099-0.
  5. [5] Rześny-Cieplińska J, Szmelter-Jarosz A. Environmental sustainability in city logistics measures. Energies. 2020;13: 1303. doi: 10.3390/en13061303.
  6. [6] Tadić S, Zečević S. Modelling city logistics concepts.[Modeliranje koncepcija city logistike]. Beograd, Serbia: Faculty of Transport and Traffic Engineering, University of Belgrade; 2016.
  7. [7] Tadić S, Zečević S. Integrated planning aimed at sustainability city logistics solutions. Tehnika. 2015;65: 164–173. doi: 10.5937/tehnika1501164T.
  8. [8] Tadić S, Zečević S, Krstić M. City logistics initiatives aimed at improving sustainability by changing the context of urban area. Tehnika. 2014;69: 834–843. doi: 10.5937/tehnika1405834T.
  9. [9] Tadić S, Zečević S, Krstić M. City logistics initiatives aimed at improving sustainability within existing context of urban area. Tehnika. 2014;69: 487–495. doi: 10.5937/tehnika1403487T.
  10. [10] Zečević S, Tadić S. Cooperation models of city logistics. Transpotation and Logistics. 2005; 123–141.
  11. [11] Tadić S, Zečević S. Cooperation and consolidation of flows in city logistics. Tehnika. 2015;70: 687–694. doi: 10.5937/tehnika1504687T.
  12. [12] Raicu S, Costescu D, Burciu S. Distribution system with flow consolidation at the boundary of urban congested areas. Sustainability. 2020;12(3): 990. doi: 10.3390/su12030990.
  13. [13] Tadić S, Kovač M, Čokorilo O. The application of drones in city logistics concepts. Promet - Traffic& Transportation. 2021;33: 451–462. doi: 10.7307/ptt.v33i3.3721.
  14. [14] González-Varona JM, et al. Reusing newspaper kiosks for last-mile delivery in urban areas. Sustainability. 2020;12: 1–27. doi: 10.3390/su12229770.
  15. [15] Tadić S, Zečević S, Krstić M. A novel hybrid MCDM model based on fuzzy DEMATEL, fuzzy ANP and fuzzy VIKOR for city logistics concept selection. Expert Systems With Applications. 2014;41: 8112–8128. doi: 10.1016/j.eswa.2014.07.021.
  16. [16] Rudolph C, Gruber J. Cargo cycles in commercial transport: Potentials, constraints, and recommendations. Research in Transportation Bussiness and Management. 2017;24: 26–36. doi: 10.1016/j.rtbm.2017.06.003.
  17. [17] Strale M. The cargo tram: Current status and perspectives, the example of Brussels. In: Macharis C, Melo S, Woxenius J, Van Lier T. (eds.) Sustainable Logistics. Emerald Group Publishing Limited; 2014. p. 245–263.
  18. [18] Maes J, Sys C, Vanelslander T. City logistics by water: Good practices and scope for expansion. In: Ocampo-Martinez C, Negenborn R. (eds.) Transport of water versus transport over water. Berlin Heidelberg: Springer; 2015. p. 413–437.
  19. [19] Hai D, Xu J, Duan Z, Chen C. Effects of underground logistics system on urban freight traffic: A case study in Shanghai, China. Journal of Cleaner Production.2020;260: 121019. doi: 10.1016/j.jclepro. 2020: 121019.
  20. [20] Jeremić M, Andrejić M. Crowd logistics – A new concept in realization of logistics services. Proceedings of the The 4th Logistics International Conference – LOGIC, May 2019, Belgrade, Serbia. 2019. p. 170–179.
  21. [21] Tadić S, Veljović M, Zečević S. Crowd logistics: Household as a logistics service provider. International Journal for Traffic & Transport Engineering. 2020;12(1). doi: 10.7708/ijtte2022.12(1).08.
  22. [22] Van Duin R, et al. Evaluating new participative city logistics concepts: The case of cargo hitching. Transportation Research Procedia. 2019;39: 565–575. doi: 10.1016/j.trpro.2019.06.058.
  23. [23] Krstić M, Tadić S, Zečević S. Technological solutions in logistics 4.0. Ekonomika preduzeća. 2021;69(6-7): 385–401. doi: 10.5937/EKOPRE2106385K.
  24. [24] Tadejko P. Application of internet of things in logistics–current challenges. Economics Management. 2015;7: 54–64. doi: 10.12846/j.em.2015.04.07.
  25. [25] Lu Y, Papagiannidis S, Alamanos E. Internet of things: A systematic review of the business literature from the user and organisational perspectives. Technological Forecasting and Social Change. 2018;136: 285–297. doi: 10.1016/j.techfore.2018.01.022.
  26. [26] Woschank M, Rauch E, Zsifkovits H. A review of further directions for artificial intelligence, machine learning, and deep learning in smart logistics. Sustainability. 2020;12: 3760. doi: 10.3390/su12093760.
  27. [27] Novais L, Marin JMM, Moyano-Fuentes J. Lean Production implementation, Cloud-Supported Logistics and Supply Chain Integration: Interrelationships and effects on business performance. International Journal of Logistics Management. 2020;31: 629–663. doi: 10.1108/IJLM-02-2019-0052.
  28. [28] Perboli G, Musso S, Rosano M. Blockchain in logistics and supply chain: A Lean approach for designing real-world use cases. IEEE Access. 2018;6: 62018–62028. doi: 10.1109/ACCESS.2018.2875782.
  29. [29] Rejeb A, Keogh J, Wamba SF, Treiblmaier H. The potentials of augmented reality in supply chain management: A state-of-the-art review. Management Review Quarterly. 2020;71: 819–856. doi: 10.1007/s11301-020-00201-w.
  30. [30] Figliozzi MA. Carbon emissions reductions in last mile and grocery deliveries utilizing air and ground autonomous vehicles. Transportation Research Part D: Transport and Environment. 2020;85: 102443. doi: 10.1016/j.trd.2020.102443.
  31. [31] Kovač M, Tadić S, Krstić M, Bouraima MB. Novel spherical fuzzy MARCOS method for assessment of drone-based city logistics concepts. Complexity. 2021;(18): 1–17. doi: 10.1155/2021/2374955.
  32. [32] Lee H-Y, Murray CC. Robotics in order picking: Evaluating warehouse layouts for pick, place, and transport vehicle routing systems. International Journal of Production Research. 2019;57: 1–21. doi: 10.1080/00207543.2018.1552031.
  33. [33] Tadić S, ZečevićS, Krstić M. Assessment of the political city logistics initiatives sustainability. Transportation Research Procedia. 2018;30: 285–294. doi: 10.1016/j.trpro.2018.09.031.
  34. [34] Krstić M, et al. A novel hybrid MCDM model for the evaluation of sustainable last mile solutions. Mathematical Problems in Engineering. 2021: 5969788. doi: 10.1155/2021/5969788.
  35. [35] Tadić S, Zečević S, Krstić M. Ranking of logistics system scenarios for central business district. Promet – Traffic& Transportation. 2014;26: 159–167. doi: 10.7307/ptt.v26i2.1349.
  36. [36] Van Heeswijk W, Larsen R, Larsen A. An urban consolidation center in the city of Copenhagen: A simulation study. International Journal of Sustainable Transportation. 2019;13: 675–691. doi: 10.1080/15568318.2018.1503380.
  37. [37] Zavadskas EK, Govindan K, Antucheviciene J, Turskis Z. Hybrid multiple criteria decision-making methods: A review of applications for sustainability issues. Economic research - Ekonomska istraživanja. 2016;29(1): 857-887. doi: 10.1080/1331677X.2016.1237302.
  38. [38] Keshavarz-Ghorabaee M, Zavadskas EK, Turskis Z, Antucheviciene J. A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. Economic Computation and Economic Cybernetics Studies Research. 2016;50: 25–44.
  39. [39] Rezaei J. Best-worst multi-criteria decision-making method. Omega. 2015;53: 49–57. doi: 10.1016/j.omega.2014.11.009.
  40. [40] Krstić M, Tadić S, Brnjac N, Zečević S. Intermodal terminal handling equipment selection using a fuzzy multi-criteria decision-making model. Promet – Traffic &Transportation. 2019;31: 89–100. doi: 10.7307/ptt.v31i1.2949.
  41. [41] Moslem S, et al. Best-worst method for modelling mobility choice after COVID-19: Evidence from Italy. Sustainability. 2020;12: 6824. doi: 10.3390/su12176824.
  42. [42] Ecer F, Pamucar D. 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. 2020;266: 121981. doi: 10.1016/j.jclepro.2020.121981.
  43. [43] Dwivedi R, et al. Performance evaluation of an insurance company using an integrated Balanced Scorecard (BSC) and Best-Worst Method (BWM). Decision Making: Applications in Management and Engineering. 2021;4: 33–50. doi: 10.31181/dmame2104033d.
  44. [44] Tadić S, Krstić M, Roso V, Brnjac N. Dry port terminal location selection by applying the hybrid grey MCDM model. Sustainability. 2020;12: 1–22. doi: 10.3390/su12176983.
  45. [45] Wei C, Wu J, Guo Y, Wei G. Green supplier selection based on CODAS method in probabilistic uncertain linguistic environment. Technological and Economic Development Economy. 2021;27: 530–549. doi: 10.3846/tede.2021.14078.
  46. [46] Perez-Dominguez L, Duran S-NA, Lopez RR, Perez-Olguin IJC, Luviano-Cruz D, Gomez JAH. Assessment urban transport service and Pythagorean Fuzzy Sets CODAS method: A case of study of Ciudad Juárez. Sustainability. 2021;13: 1281. doi: 10.3390/su13031281.
  47. [47] Kiracı K, Bakır M. Evaluation of airlines performance using an integrated critic and CODAS methodology: The case of Star Alliance member airlines. Studies in Business Economics. 2020;15: 83–99. doi: 10.2478/sbe-2020-0008.
  48. [48] Yang Y, John R. Grey sets and greyness. Information Sciences. 2012;185: 249–264. doi: 10.1016/j.ins.2011.09.029.
  49. [49] Mahmoudi A, et al. Grey best-worst method for multiple experts multiple criteria decision making under uncertainty. Informatica. 2020;31: 331–357. doi: 10.15388/20-INFOR409.
  50. [50] Taniguchi E. City logistics for sustainable and liveable cities. In: Fahimnia B, Bell MGH, Hensher DA, Sarkis J. (eds.) Green logistics and transportation: A sustainable supply chain perspective. Springer; 2015. p. 49–60.
  51. [51] DeutschN, Berenyi L. Personal approach to sustainability of future decision makers: A Hungarian case. Environment, Development and Sustainability. 2018;20: 271–303. doi: 10.1007/s10668-016-9881-9.
  52. [52] Awasthi A, Chauhan SS. A hybrid approach integrating affinity diagram, AHP and fuzzy TOPSIS for sustainable city logistics planning. Applied Mathematical Modelling. 2012;36: 573–584. doi: 10.1016/j.apm.2011.07.033.
  53. [53] Osati M, Omidvari M. Performance measurement of electricity suppliers using PROMETHEE and balance scorecard. Management Science Letters. 2016;6: 387–394. doi: 10.5267/j.msl.2016.4.007.
  54. [54] Mukherjee S, Ghosh B. Application of Grey Possibility Degree in Comparing Poverty. International Journal of Innovative Research Science Engineeringand Technology. 2015;4: 4698–4703. doi: 10.15680/IJIRSET.2015.0406145.
  55. [55] Zečević S, Tadić S. City logistics. [City logistika]. Beograd, Serbia: Faculty of Transport and Traffic Engineering, University of Belgrade; 2006.
Show more

SPECIAL ISSUE CALL

Innovation and New Technologies in Transport and Logistics

Guest Editor: Eleonora Papadimitriou, PhD

Editors: Marko Matulin, PhD; Dario Babić, PhD; Marko Ševrović, PhD.

RECENT PAPERS

Browse through the selection of our recent research
College Students’ Cognition and Attitude Towards Connected and Autonomous Vehicles in China: an Exploratory StudyA Study on the Impact of Overtaking Lane-Changing Behaviour in Expressway Interchange Weaving AreasRisk Analysis of Level Crossing Element Failures in a Fuzzy EnvironmentImplications of Retrograde Behaviours on Visual and Cycling Behaviour of Normal Cyclists

Accelerating Discoveries in Traffic Science |
2024 © Promet - Traffic&Transportation journal