Skip to main content
SpringerLink
Log in
Book cover

International Conference on Computational Logistics

ICCL 2017: Computational Logistics pp 253–269Cite as

Review of Fuzzy Techniques in Maritime Shipping Operations

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10572))

Abstract

Fuzzy Logic has found significant interest in the context of global shipping networks due to its applicability to uncertain decision making environments. Its use has been particularly important when solving location and equipment selection problems. While being applicable as a stand-alone technique, Fuzzy Logic has become increasingly interesting as an added feature within classic Operational Research techniques. This paper gives an outline of the methodological relevance of Fuzzy Logic at a strategic, tactical and operational level for maritime operations. In addition, a general classification of decision problems in maritime logistics is presented, extending previous classifications in the literature to the wider context of multiple port networks.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Balmat, J.-F., Frédéric, L., Maifret, R., Pessel, N.: Maritime risk assessment (marisa), a fuzzy approach to define an individual ship risk factor. Ocean Engineering 36, 1278–1286 (2009)

    Article  Google Scholar 

  2. Balmat, J.-F., Frédéric, L., Maifret, R., Pessel, N.: A decision-making system to maritime risk assessment. Ocean Engineering 38, 171–176 (2011)

    Article  Google Scholar 

  3. Benayoun, R., Roy, B., Sussman, B.: Electre: une méthode pour guider le choix en présence des point de vue multiples. Technical report (1966)

    Google Scholar 

  4. Bierwirth, C., Meisel, F.: A survey of berth allocation and quay crane scheduling problems in container terminals. European Journal of Operations Research 202, 615–627 (2010)

    Article  MATH  Google Scholar 

  5. Böse, J.W.: Operations research/computer science interfaces series. In: Handbook of terminal planning. Springer, Heidelberg (2011)

    Google Scholar 

  6. Celik, M., Cebi, S., Kahraman, C., Er, I.D.: Application of axiomatic design and topsis methodologies uner fuzzy environment for proposing competitive strategies on turkish container ports in maritime transportation network. Expert Systems with Applications 36, 4541–4557 (2009)

    Article  Google Scholar 

  7. Chao, S.-L.: Integrating multi-stage data envelopment analysis and a fuzzy analytical hierarchical process to evaluate the efficiency of major global liner shipping companies. Maritime Policy & Management, 1–16 (2017)

    Google Scholar 

  8. Chao, S.-L., Lin, Y.-J.: Evaluating advanced quay cranes in container terminals. Transport Researc Part E: Logistics and Transportation Review 47(4), 432–445 (2011)

    Article  Google Scholar 

  9. Chen, C.-A., Chiang, Y.-H., Hsu, T.-K., Hsia, J.-W.: Strategies to increase the competitiveness of taiwans free trade ports based on the fuzzy importance-performance analysis. Asian Economic and Financial Review 6(11), 681 (2016)

    Article  Google Scholar 

  10. Chiu, R.-H., Lin, L.-H., Ting, S.-C.: Evaluation of green port factors and performance: A fuzzy ahp analysis. Mathematical Problems in Engineering (2014)

    Google Scholar 

  11. Cho, G.-S., Hwang, H.-S., Lee, K.-W.: A performance analysis framework for the container terminals by dhp method. In: International Conference on Intelligent Manufacturing and Logistics Systems IML, Kitakyushu, Japan (2007)

    Google Scholar 

  12. Chou, C.-C.: A fuzzy mcdm method for solving marine transshipment container port selection problems. Applied Mathematics and Computation 186, 435–444 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  13. Chou, C.-C.: Application of FMCDM model to selecting the hub location in the marine transportation: A case study in southeastern asia. Mathematical and Computer Modelling 51, 791–801 (2010)

    Article  MATH  Google Scholar 

  14. Chou, C.C.: A fuzzy backorder inventory model and application to determining the optimal empty-container quantity at a port. International Journal of Innovative Computing, Innovation and Control 5, 4825–4824 (2009)

    Google Scholar 

  15. Chou, C.C., Gou, R.-H., Tsai, C.-L., Tsou, M.-C., Wong, C.P., Yu, H.L.: Application of a mixed fuzzy decision making and optimization programming model to the empty container allocation. Applied Soft Computing 10, 1071–1079 (2010a)

    Article  Google Scholar 

  16. Chou, C.C., Kuo, F.-T., Gou, R.-H., Tsao, C.-L., Wong, C.-P., Tsou, M.-C.: Application of a combined fuzzy multiple criteria decision making and optimization programming model to the container transportation demand split. Applied Soft Computing 10, 1080–1086 (2010b)

    Article  Google Scholar 

  17. Chuang, T.-N., Lin, C.-T., Kung, J.-Y., Lin, M.-D.: Planning the route of container ships: A fuzzy genetic approach. Expert Systems with Applications 37, 2948–2956 (2010)

    Article  Google Scholar 

  18. Chung, S.H., Chan, F.T.S.: A workload balancing genetic algorithm for the quay crane scheduling problem. International Journal of Production Research 51 (2013)

    Google Scholar 

  19. Denisis, A.: An economic feasibility study of short sea shipping including the estimation of externalities with fuzzy logic. PhD thesis, University of Michigan (2009)

    Google Scholar 

  20. Ding, J.F., Chou, C.-C.: A fuzzy mcdm model to evaluate investment risk of location selection for container terminals. WSEAS Transactions on Information Science and Applications 9(10), 295–304 (2012)

    Google Scholar 

  21. Duru, O., Bulut, E., Yoshid, S.: Bivariate long term fuzzy time series forecasting of dry cargo freight rates. The Asian Journal of Shipping and Logistics 26(2), 205–223 (2010)

    Article  Google Scholar 

  22. Ergin, A., Eker, İ., Alkan, G.: Selection of container port using electre technique. Management 4(4), 268–275 (2015)

    Google Scholar 

  23. Expósito-Izquiero, C., Lalla-Ruiz, E., Lamata, T., Melián-Batista, B., Moreno-Vega, J.M.: Fuzzy optimization models for seaside port logistics: berthing and quay crane scheduling. In: Madani, K., Dourado, A., Rosa, A., Filipe, J., Kacprzyk, J. (eds.) Computational Intelligence. SCI, vol. 613, pp. 323–343. Springer, Cham (2016). doi:10.1007/978-3-319-23392-5_18

    Chapter  Google Scholar 

  24. Gaonkar, R.S.P., Xie, M., Fu, X.: Reliability estimation of maritime transportation: A study of two fuzzy reliability models. Ocean Engineering 72, 1–10 (2013)

    Article  Google Scholar 

  25. Ghazanfari, M., Rouhani, S., Jafari, M.: A fuzzy topsis model to evaluate the business intelligence competencies of port community systems. Polish Maritime Research 21(2), 86–96 (2014)

    Article  Google Scholar 

  26. Giuliano, G., O’Brien, T.: Reducing port-related truck emissions: The terminal gate appointment system at the ports of los angeles and long beach. Transportation Research Part D: Transport and Environment 12, 460–473 (2007)

    Article  Google Scholar 

  27. Ha, M.-H., Yang, Z., Notteboom, T., Ng, A.K.Y., Heo, M.-W.: Revisiting port performance measurement: A hybrid multi-stakeholder framework for the modelling of port performance indicators. Transportation Research Part E: Logistics and Transportation Review 103, 1–16 (2017)

    Article  Google Scholar 

  28. He, S., Song, R., Chaudhry, S.S.: Fuzzy dispatching model and genetic algorithms for railyards operations. European Journal of Operational Research 124, 307–331 (2000)

    Article  MATH  Google Scholar 

  29. Homayouni, S.M., Hong, S.: A fuzzy genetic algorithm for scheduling of handling/storage equipment in automated container terminals. International Journal of Engineering and Technology 7(6), 497–501 (2015)

    Article  Google Scholar 

  30. Hsu, W.-K.K., Yu, H.-F., Huang, S.-H.S.: Evaluating the service requirements of dedicated container terminals: a revised ipa model with fuzzy ahp. Maritime Policy & Management 42(8), 789–805 (2015)

    Article  Google Scholar 

  31. Hwang, C.L., Yoon, K.: Multiple Attribute Decision Making: Methods and Applications. Springer, New York (1981)

    Book  MATH  Google Scholar 

  32. Jafari, H., Saeidi, N., Kaabi, A., Noshadi, E., Hallafi, H.R.: Analysis of performance in container handling operation by using fuzzy topsis method. International Review of Basic and Applied Sciences 1(6), 148–155 (2013)

    Google Scholar 

  33. Jin, C., Liu, X., Gao, P.: An intelligent simulation method based on artificial neural network for container yard operation. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3174, pp. 904–911. Springer, Heidelberg (2004). doi:10.1007/978-3-540-28648-6_144

    Chapter  Google Scholar 

  34. John, A., Paraskevadakis, D., Bury, A., Yang, Z., Riahi, R., Wang, J.: An integrated fuzzy risk assessment for seaport operations. Safety Science 68, 180–194 (2014)

    Article  Google Scholar 

  35. Ka, B.: Application of fuzzy AHP and ELECTRE to China Dry port location selection. The Asian Journal of Shipping and Logistics 27, 331–335 (2011)

    Article  Google Scholar 

  36. Kayikci, Y.: A conceptual model for intermodal freight logistics centre location decisions. Procedia-Social and Behavioral Sciences 2, 6297–6311 (2010)

    Article  Google Scholar 

  37. Kim, Y.H., Park, T., Ryu, K.R.: Dynamic weight adjustment for developing a stacking policy for automated container terminals. In: International Conference on Intelligent Manufacturing and Logistics Systems (IML 2007), Kitakyushu, Japan, pp. 26–28 (2007)

    Google Scholar 

  38. Ko, H.J.: A dss approach with fuzzy ahp to facilitate international multimodal transportation network. KMI International Journal of Maritime Affairs and Fisheries 1, 51–70 (2009)

    Google Scholar 

  39. Liang, G.S., Ding, J.-F., Wang, C.-K.: Applying fuzzy quality function deployment to prioritize solutions of knowledge management for an international port in Taiwan. Knowledge-Based Systems 33, 83–91 (2012)

    Article  Google Scholar 

  40. Liu, D., Yi, J., Zhao, D., Wang, W.: Adaptive sliding mode fuzzy control for a two-dimensional overhead crane. Mechatronics 15(5), 505–522 (2005)

    Article  Google Scholar 

  41. Liu, W., Xu, H., Zhao, X.: Agile service oriented shipping companies in the container terminal. Transport 24(2), 143–153 (2009)

    Article  Google Scholar 

  42. Lokuge, P., Alahakoon, D.: Improving the adaptability in automated vessel scheduling in container ports using intelligent software agents. European Journal of Operational Research 177, 1985–2015 (2007)

    Article  MATH  Google Scholar 

  43. Lokuge, P., Alahakoon, D., Dissanayake, P.: Collaborative neuro-BDI agents in container terminals. In: 18th International Conference on Advanced Information Networking and Application, AINA, pp. 155–158 (2004)

    Google Scholar 

  44. Mabrouki, C., Bentaleb, F., Mousrij, A.: A decision support methodology for risk management within a port terminal. Safety Science 63, 124–132 (2014)

    Article  Google Scholar 

  45. Mi, X.-Y., Cheng, G.: Railway container center door lane analysis based on \(\upalpha \)-cut theory. Procedia - Social and Behavioral Sciences 96(6), 2425–2430 (2013)

    Article  Google Scholar 

  46. Ng, W.C., Ge, Y.: Scheduling landside operations of a container terminal using a fuzzy heuristic. In: IEEE Industrial Conference on Industrial Informatics (2006)

    Google Scholar 

  47. Nooramin, A.S., Kiani, M., Mansoor, M., Jahromi, A.R., Sayareh, J.: Comparison of ahp and fahp for selecting yard gantry cranes in marine container terminals. Journal of the Persian Gulf (Marine Science) 3(7), 50–70 (2012)

    Google Scholar 

  48. Onut, S., Tuzkaya, U.R., Torun, E.: Selecting container port via a fuzzy ANP-based approach: A case study in the Marmara region, Turkey. Transport Policy 18, 181–193 (2010)

    Google Scholar 

  49. Park, J.-Y., Yeo, G.-T.: An evaluation of greenness of major Korean ports: A fuzzy set approach. The Asian Journal of Shipping and Logistics 28, 67–82 (2012)

    Article  Google Scholar 

  50. Ran, W., Xu, Z., Weihong, Z.: Analysis on comprehensive strength of Chinese coastal container shipping company based on genetic fuzzy clustering. In: Proceedings of the IEEE International Conference on Automation and Logistics, Qingdao, China, pp. 2214–2219 (2008)

    Google Scholar 

  51. Riedewald, F.: Comparison of deterministic, stochastic and fuzzy logic uncertainty modelling for capacity extension projects of DI/WFI pharmaceutical plant utilities with variable/dynamic demand. PhD thesis, University College Cork, Ireland (2011)

    Google Scholar 

  52. Ries, J., González-Ramírez, R.G., Miranda, P.: A fuzzy logic model for the container stacking problem at container terminals. In: González-Ramírez, R.G., Schulte, F., Voß, S., Ceroni Díaz, J.A. (eds.) ICCL 2014. LNCS, vol. 8760, pp. 93–111. Springer, Cham (2014). doi:10.1007/978-3-319-11421-7_7

    Google Scholar 

  53. Saaty, T.: A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology 15, 234–281 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  54. Saeidi, N., Askari, A., Jafari, H.: Application of a fuzzy topsis approach based on subjective and objective weights in the container terminals risks assessment. Applied Mathematics in Engineering, Management and Technology 1(4), 2013 (2013)

    Google Scholar 

  55. Seyed-Hosseini, S.-M., Damghani, K.K.: Fuzzy container allocation problem in maritime terminal. Journal of Industrial Engineering and Management 2(2), 323 (2009)

    Article  Google Scholar 

  56. Shao, W., Du, Y., Lu, S.: Performance evaluation of port supply chain based on fuzzy-matter-element analysis. Journal of Intelligent & Fuzzy Systems 31(4), 2159–2165 (2016)

    Article  Google Scholar 

  57. Stahlbock, R., Voß, S.: Operations research at container terminals: a literature update. OR Spectrum 30, 1–52 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  58. Steenken, D., Voß, S., Stahlbock, R.: Container terminal operation and operations research - a classification and literature review. OR Spectrum 26, 3–49 (2004)

    Article  MATH  Google Scholar 

  59. Tierney, K., Voß, S., Stahlbock, R.: A mathematical model of inter-terminal transportation. European Journal of Operational Research 235, 448–460 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  60. Torfi, F., Farahani, R.Z., Rezapour, S.: Fuzzy AHP to determine the relative weights of evaluation criteria and fuzzy topsis to rank the alternatives. Applied Soft Computing 10, 520–528 (2010)

    Article  Google Scholar 

  61. Tuljak-Suban, D., Twrdy, E.: Fuzzy empty containers excess estimation as an economic indicator—the case of the north adriatic port system. Maritime Policy & Management 42(8), 759–775 (2015)

    Article  Google Scholar 

  62. Ung, S.T., Williams, V., Chen, H.S., Bonsall, S., Wang, J.: Human error assessment and management in port operations using fuzzy ahp. Marine Technology Society Journal 40, 73–86 (2006)

    Article  Google Scholar 

  63. Valdés-González, H., Reyes-Bozo, L., Vyhmeister, E., Salazar, J.L., Sepúlveda, J.P., Mosca-Arestizábal, M.: Container stacking revenue management system: A fuzzy-based strategy for Valparaiso port. Dyna 82(190), 38–45 (2015)

    Article  Google Scholar 

  64. Vukadinović, K., Teodorovíc, D.: A fuzzy approach to the vessel dispatching problem. European Journal of Operational Research 76, 155–164 (1994)

    Article  MATH  Google Scholar 

  65. Wang, B.: Research about the fuzzy optimization of repositioning of empty container on sea-bound. Port Engineering Technology (2007)

    Google Scholar 

  66. Wang, Y., Yeo, G.-T., Ng, A.K.Y.: Choosing optimal bunkering ports for liner shipping companies: A hybrid fuzzy-delphi-topsis approach. Transport Policy 35, 358–365 (2014)

    Article  Google Scholar 

  67. Wanke, P., Falcão, B.B.: Cargo allocation in Brazilian ports: An analysis through fuzzy logic and social networks. Journal of Transport Geography 60, 33–46 (2017)

    Article  Google Scholar 

  68. Wibowo, S., Deng, H.: A fuzzy screening system for effectively solving maritime shipping problems, Coimbra, Portugal (2010)

    Google Scholar 

  69. Wibowo, S., Deng, H.: Intelligent decision support for criteria weighting in multicriteria analysis for evaluating and selecting cargo ships under uncertainty. In: International MultiConference of Engineers and Computer Scientists, IMECS, Hong Kong, (2011)

    Google Scholar 

  70. Yang, Z., Ng, A.K.Y., Wang, J.: A new risk quantification approach in port facility security assessment. Transportation Research Part A: Policy and Practice 59, 72–90 (2014)

    Google Scholar 

  71. Yang, Z.L., Bonsall, S., Wang, J.: Use of hybrid multiple uncertain attribute decision making techniques in safety management. Expert Systems with Applications 36, 1569–1586 (2009)

    Article  Google Scholar 

  72. Yasunobu, S., Hasegawa, T.: Evaluation of an automatic con-tainer crane operation sys-tem based on predictive fuzzy control. Control Theory and Advanced Technology 2(3), 419–432 (1986)

    Google Scholar 

  73. Yeo, G.-T., Song, D.-W.: An application of the hierarchical fuzzy process to container port competition: Policy and strategic implications. Transportation 33, 409–422 (2006)

    Article  Google Scholar 

  74. Yeo, G.-T., Ng, A.K.Y., Lee, P.T.-W., Yang, Z.: Modelling port choice in an uncertain environment. Maritime Policy & Management 41(3), 251–267 (2014)

    Article  Google Scholar 

  75. Yu, M., Wang, S., Yun, C.: A dispatching method for trucks at container terminal by using fuzzy-cnp concept. In: IIEEE International Conference on Logistics Engineering and Intelligent Transportation Systems, LEITS, Wuhan, pp. 1–4 (2010)

    Google Scholar 

  76. Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  77. Zavadskas, E.K., Turskis, Z., Bagočius, V.: Multi-criteria selection of a deep-water port in the eastern baltic sea. Applied Soft Computing 26, 180–192 (2015)

    Article  Google Scholar 

  78. Zehendner, E., Feillet, D.: Benefits of a truck appointment system on the service quality of inland transport modes at a multimodal container terminal. European Journal of Operational Research 235, 461–469 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  79. Zheng, J.-N., Chien, C.-F., Gen, M.: Multi-objective multi-population biased random-key genetic algorithm for the 3-D container loading problem. Computers & Industrial Engineering, 80–87 (2015)

    Google Scholar 

  80. Zhou, P., Kang, H., Li, L.: A fuzzy model for scheduling handling equipments handling outbound container in terminal. In: Sixth World Congress on Intelligent Control and Automation (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Portsmouth Business School, University of Portsmouth, Portland Street, Portsmouth, PO1 3DE, UK

    Jana Ries

  2. Faculty of Engineering and Applied Sciences, Universidad de Los Andes, Chile, Monsenor Alvaro Portillo, 12450, Santiago, Chile

    Rosa G. González-Ramírez

  3. Institute of Information Systems, University of Hamburg, Von-Melle-Park 5, 20146, Hamburg, Germany

    Stefan Voß

Authors
  1. Jana Ries
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rosa G. González-Ramírez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefan Voß
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rosa G. González-Ramírez .

Editor information

Editors and Affiliations

  1. University of Southampton , Southampton, United Kingdom

    Tolga Bektaş

  2. University of Southampton , Southampton, United Kingdom

    Stefano Coniglio

  3. University of Southampton , Southampton, United Kingdom

    Antonio Martinez-Sykora

  4. University of Hamburg , Hamburg, Germany

    Stefan Voß

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Ries, J., González-Ramírez, R.G., Voß, S. (2017). Review of Fuzzy Techniques in Maritime Shipping Operations. In: Bektaş, T., Coniglio, S., Martinez-Sykora, A., Voß, S. (eds) Computational Logistics. ICCL 2017. Lecture Notes in Computer Science(), vol 10572. Springer, Cham. https://doi.org/10.1007/978-3-319-68496-3_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68496-3_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68495-6

  • Online ISBN: 978-3-319-68496-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation