Intermodal Terminal Handling Equipment Selection Using a Fuzzy Multi-criteria Decision-making Model
Intermodal transport enables energy, costs and time savings, improves the service quality and supports sustainable development. The basic element of the intermodal transport system is an intermodal terminal, whose efficiency largely depends on the subsystems’ technologies. Accordingly, the topic of this paper is the evaluation and the selection of the appropriate handling equipment within the intermodal terminal. As the decision-making on the handling equipment is influenced by different economic, technical, technological and other criteria, the appropriate multi-criteria decision-making (MCDM) methods have to be applied in order to solve the problem. In this paper, a novel hybrid model which combines the fuzzy step-wise weight assessment ratio analysis (FSWARA) and the fuzzy best-worst method (FBWM) is developed. The defined model is applied for solving the case study of selecting adequate handling equipment for the planned intermodal terminal in Belgrade. The reach stacker is selected as the most adequate handling equipment since it suits best the characteristics of the planned terminal in the given conditions and in relation to the defined criteria. Solving the case study demonstrated the justification for using the MCDM methods to solve these kinds of problems as well as the applicability of the proposed MCDM model.
Barysienė J. A multi-criteria evaluation of container terminal technologies applying the COPRAS-G method. Transport. 2012;27(4): 364–372.
European Commission (EC). White Paper: Roadmap to a Single European Transport Area – Towards a Competitive and Resource Efficient Transport System. Brussels; 2011 Available from: http:// eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX: 52011DC0144:EN:NOT [Accessed 16th March 2018]
ECMT - European Conference of Ministers of Transport. Terminology on combined transport. Brussels; 1993. Available from ftp://ftp.cordis.europa.eu/pub/transport/docs/ intermodal_freight transport_en.pdf. [Accessed 16th March 2018]
UNECE - United Nations Economic Commission for Europe. Illustrated glossary for transport statistics. Luxembourg: Publications Office of the European Union; 2009. Available from: http://www.unece.org/fileadmin/ DAM/trans/main/ wp6/pdfdocs/glossen4.pdf. [Accessed 16th March 2018]
Lau HYK, Zhao Y. Integrated scheduling of handling equipment at automated container terminals. International Journal of Production Economics. 2008;112: 665–682. Available from: 10.1016/j.ijpe.2007.05.015
Keršuliene V, Zavadskas EK, Turskis Z. Selection of rational dispute resolution method by applying new step wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management. 2010;11: 243-258.
Alimardani M, Zolfani SH, Aghdaie MH, Tamošaitienė J. A novel hybrid SWARA and VIKOR methodology for supplier selection in an agile environment. Technological and Economic Development of Economy. 2013;19: 533-548.
Zolfani SH, Zavadskas EK, Turskis Z. Design of products with both International and Local perspectives based on Yin-Yang balance theory and SWARA method. Economic Research-Ekonomska Istraživanja. 2013;26: 153-166.
Zolfani SH, Saparauskas J. New Application of SWARA Method in Prioritizing Sustainability Assessment Indicators of Energy System. Engineering Economics. 2013;24: 408-414.
Aghdaie MH, Zolfani SH, Zavadskas EK. Decision making in machine tool selection: An integrated approach with SWARA and COPRAS-G methods. Engineering Economics. 2013;24: 5-17.
Dehnavi A, Aghdam IN, Pradhan B, Varzandeh MHM. A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. Catena. 2015;135: 122–148. Available from: doi: 10.1016/j.catena.2015.07.020.
Mardani A, Nilashi M, Zakuan N, Loganathan N, Soheilirad S, Saman MZM, Ibrahim O. A systematic review and meta-Analysis of SWARA and WASPAS methods: Theory and applications with recent fuzzy developments. Applied Soft Computing. 2017;57: 265-292. Available from: doi: 10.1016/j.asoc.2017.03.045.
Zadeh LA. Fuzzy sets. Information & Control. 1965;8: 338–353.
Rezaei J. Best-Worst Multi-Criteria Decision-Making Method. Omega. 2015;53: 49-57. Available from: doi: 10.1016/j.omega.2014.11.009.
Ahmadi HB, Kusi-Sarponga S, Rezaeic, J. Assessing the social sustainability of supply chains using Best Worst Method. Resources. Conservation & Recycling. 2017;126: 99–106. Available from: doi: 10.1016/j.resconrec.2017.07.020.
Ahmad WNKW, Rezaei J, Sadaghiani S, Tavasszy LA. 2017. Evaluation of the external forces affecting the sustainability of oil and gas supply chain using Best Worst Method. Journal of Cleaner Production. 2017;153: 242-252. Available from: doi: 10.1016/j.jclepro.2017.03.166.
Gupta H, Barua MK. Supplier selection among SMEs on the basis of their green innovation ability using BWM and fuzzy TOPSIS. Journal of Cleaner Production. 2017;152: 242-258. Available from: doi: 10.1016/j.jclepro.2017.03.125.
Rezaei J, Nispeling T, Sarkis J, Tavasszy L. A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. Journal of Cleaner Production. 2016;135: 577-588. Available from: doi: 10.1016/j.jclepro.2016.06.125.
Gupta H. Evaluating service quality of airline industry using hybrid best worst method and VIKOR. Journal of Air Transport Management. 2017;in press: xx-xx. Available from: doi: 10.1016/j.jairtraman.2017.06.001.
Ren J, Liang H, Chan FTS. Urban sewage sludge, sustainability, and transition for Eco-City: Multi-criteria sustainability assessment of technologies based on best-worst method. Technological Forecasting & Social Change. 2017;116: 29–39.
Shojaei P, Haeri SAS, Mohammadi S. Airports evaluation and ranking model using Taguchi loss function, best-worst method and VIKOR technique. Journal of Air Transport Management. 2017; in press: 1-10. Available from: doi: 10.1016/j.jairtraman.2017.05.006.
Herman MW, Koczkodaj WW. A Monte Carlo study of pairwise comparison. Information. Processing Letters. 1996;57: 25-9. Available from: doi: 10.1016/0020-0190(95)00185-9.
Guo S, Zhao H. Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems. 2017;121: 1–9. Available from: doi: 10.1016/j.knosys.2017.01.010.
Mou Q, Xu Z, Liao H. 2016. An intuitionistic fuzzy multiplicative best-worst method for multi-criteria group decision making. Information Sciences. 2016;374: 224–239. Available from: doi: 10.1016/j.ins.2016.08.074.
Roso V, Woxenius J, Lumsden K. The dry port concept: connecting container seaports with the hinterland. Journal of Transport Geography. 2009;17(5): 338–345. Available from: doi: 10.1016/j.jtrangeo.2008.10.008.
Woxenius J. Alternative transport network designs and their implications for intermodal transhipment technologies. European Transport – Trasporti Europei. 2007;35: 27–45.
Sirikijpanichkul A, Ferreira L. 2005. Multi-objective evaluation of intermodal freight terminal location decisions, In: (eds.) Proceedings of 27th conference: Australian institute of transport research (CAITR), 7-9 December 2005, Queensland University of Technology (QUT), Brisbane.
Lee BK, Kim KH. Optimizing the block size in container yards. Transportation Research Part E: Logistics and Transportation Review. 2010;46(1): 120–135. Available from: doi: 10.1016/j.tre.2009.07.001.
Golbabaie F, Seyedalizadeh Ganji SR, Arabshahi N. 2012. Multi-criteria evaluation of stacking yard configuration. Journal of King Saud University – Science. 2012;24(1): 39–46. Available from: doi: 10.1016/j.jksus.2010.08.010.
Stahlbock R, Voß S. Operations research at container terminals: a literature update. OR Spectrum. 2008;30(1): 1–52. Available from: doi: 10.1007/s00291-007-0100-9.
Steenken D, Voß S, Stahlbock R. Container terminal operation and operations research – a classification and literature review. OR Spectrum. 2004;26(1): 3–49. Available from: doi: 10.1007/s00291-003-0157-z.
Hsu WKK. Improving the service operations of container terminals. The International Journal of Logistics Management. 2013;24(1): 101 - 116. Available from: doi: 10.1108/IJLM-05-2013-0057.
Zečević S, Tadić S, Krstić M. Intermodal transport terminal location selection using a novel hybrid MCDM model. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 2017;25(6): 853-876. Available from: doi: 10.1142/S0218488517500362.
Roso V, Brnjac N, Abramovic B. Inland Intermodal Terminals Location Criteria Evaluation: The Case of Croatia. Transportation journal. 2015;54(4): 496-515. Available from: doi: 10.5325/transportationj.54.4.0496.
Vidović M, Zečević S, Kilibarda M, Vlajić J, Bjelić N, Tadić S. The p-hub model with hub-catchment areas, existing hubs, and simulation: a case study of Serbian intermodal terminals. Networks and Spatial Economics. 2011; 11(2): 295-314. Available from: doi: 10.1007/s11067-009-9126-7.
Garcia TR, Cancelas NG, Soler-Flores F. Setting the port planning parameters in container terminals through bayesian networks. Promet – Traffic&Transportation. 2015;27(5): 395-403. Available from: doi: 10.7307/ptt.v27i5.1689.
Stojkovic M, Twrdy E. A decision support tool for container terminal optimization within the berth subsystem. Transport. 2015;31(1): 29-40.
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(18): 8112-8128. Available from: doi: 10.1016/j.eswa.2014.07.021.
Zhang Q, Zeng Q, Yang H. A lexicographic optimization approach for berth schedule recovery problem in container terminals. Transport. 2016;31(1): 76–83.
Kunadhamraks P, Hanaoka S. Evaluation of logistics performance for freight mode choice at an intermodal terminal, In: Taniguchi E, Thompson RG (Eds.) Recent Advances in City Logistics, Elsevier Science Ltd: 2005. p. 191–205.
Wang Y. Performance Evaluation of International Container Ports in Taiwan and Neighborhood Area by Weakness and Strength Indices of FMCDM. Journal of Testing and Evaluation. 2016;44(5): 1840-1852. Available from: doi: 10.1520/jte20140326.
Carlo HJ, Vis IFA, Roodbergen KJ. Storage yard operations in container terminals: Literature overview, trends, and research directions. European Journal of Operational Research. 2014a;235: 412–430. Available from: doi: 10.1016/j.ejor.2013.10.054.
Carlo HJ, Vis IFA, Roodbergen KJ. Transport operations in container terminals: Literature overview, trends, research directions and classification scheme. European Journal of Operational Research. 2014b;236: 1–13. Available from: doi: 10.1016/j.ejor.2013.11.023.
Eliiyi DT, Mat G, Özmen B. Storage optimization for export containers in the port of Izmir. Promet – Traffic&Transportation. 2013;25(4): 359-367. Available from: doi: 10.7307/ptt.v25i4.1170.
Zhang C, Liu J, Wan Y, Murty KG, Linn RJ. Storage space allocation in container terminals. Transportation Research Part B. 2003;37: 883–903. Available from: doi: 10.1016/S0191-2615(02)00089-9.
Jurjević M, Hess S. The operational planning model of transhipment processes in the port. Promet – Traffic&Transportation. 2016;28(2): 81-89. Available from: doi: 10.7307/ptt.v28i2.1815.
Taner ME, Kulak O, Koyuncuoglu MU. Layout analysis affecting strategic decisions in artificial container terminals. Computers & Industrial Engineering. 2014;75: 1–12. Available from: doi: 10.1016/j.cie.2014.05.025.
Zaghdoud R, Mesghouni K, Dutilleul SC, Zidi K, Ghedira K. A Hybrid Method for Assigning Containers to AGVs in Container Terminal. IFAC-PapersOnLine. 2016;49(3): 96–103. Available from: doi: 10.1016/j.ifacol.2016.07.017.
Yang YC, Lin CL. Performance analysis of cargo-handling equipment from a green container terminal perspective. Transportation Research Part D. 2013;23: 9–11. Available from: doi: 10.1016/j.trd.2013.03.009.
Huang WC, Chu CY. A selection model for in-terminal container handling systems. Journal of Marine Science and Technology. 2004;12(3): 159-170. Available from: http://jmst.ntou.edu.tw/marine/12-3/159-170.pdf [Accessed 21th March 2018]
Vis IFA. A comparative analysis of storage and retrieval equipment at a container terminal. International Journal of Production Economics. 2006;103: 680–693. Available from: doi: 10.1016/j.ijpe.2006.01.002.
Kutlu AC, Ekmekcioglu M. Fuzzy failure modes and effects analysis by using fuzzy TOPSIS/based fuzzy AHP. Expert Systems with Applications. 2012;39: 61–67. Available from: doi: 10.1016/j.eswa.2011.06.044.
Zečević S. Robni terminali i robno-transportni centri, Belgrade, Faculty of Transport and Traffic Engineering, University of Belgrade; 2006. Serbian
EC Delegation to the Republic of Serbia. Facilitating Intermodal Transport in Serbia. Republic of Serbia; 2010-2012.
Copyright (c) 2019 Mladen Dragan Krstić, Snežana Radoman Tadić, Nikolina Brnjac, Slobodan Zečević
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).