Skip to main content
SpringerLink
Log in
Book cover

Soft Computing Based Optimization and Decision Models pp 1–23Cite as

A Review of Soft Computing Techniques in Maritime Logistics and Its Related Fields

  • Chapter
  • First Online:

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 360))

Abstract

The incessant increase in the world seaborne trade over the last few decades has encouraged maritime logistics has become a very attractive area of study for applying the general frameworks of soft computing. In this environment, there is a significant lack of efficient approaches aimed at obtaining exact solutions of a wide variety of optimization problems arisen in this field and which are classified as hard from the perspective of the complexity theory. These optimization problems demand increasingly new computational approaches able to report inexact solutions by exploiting extensively uncertainty, tolerance for imprecision, and partial truth to achieve tractability, among others. In the chapter at hand, we provide a review of the most highlighted soft computing techniques implemented in maritime logistics and its related fields and identify some opportunities to go further into depth on knowledge.

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   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover 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

Notes

  1. 1.

    http://unctad.org.

References

  1. Alyami, H., Yang, Z., Riahi, R., Bonsall, S., Wang, J.: Advanced uncertainty modelling for container port risk analysis. Accid. Anal. Prev. (2016). doi:10.1016/j.aap.2016.08.007

    Google Scholar 

  2. Ambrosino, D., Anghinolfi, D., Paolucci, M., Sciomachen, A.: A new three-step heuristic for the master bay plan problem. Marit. Econ. Logist. 11(1), 98–120 (2009)

    Article  Google Scholar 

  3. Angeloudis, P., Bell, M.G.H.: An uncertainty-aware AGV assignment algorithm for automated container terminals. Transp. Res. Part E: Logist. Transp. Rev. 46(3), 354–366 (2010)

    Google Scholar 

  4. Araújo, E.J., Chaves, A.A., de Salles, L.L.: Neto, and A.T. de Azevedo. Pareto clustering search applied for 3D container ship loading plan problem. Expert Syst. Appl. 44, 50–57 (2016)

    Article  Google Scholar 

  5. Avriel, M., Penn, M.: Exact and approximate solutions of the container ship stowage problem. Comput. Ind. Eng. 25(1–4), 271–274 (1993)

    Article  MATH  Google Scholar 

  6. Bakkehaug, R., Rakke, J.G., Fagerholt, K., Laporte, G.: An adaptive large neighborhood search heuristic for fleet deployment problems with voyage separation requirements. Transp. Res. Part C: Emerg. Technol. 70, 129–141 (2016)

    Article  Google Scholar 

  7. Bierwirth, C., Meisel, F.: A survey of berth allocation and quay crane scheduling problems in container terminals. Eur. J. Oper. Res. 202(3), 615–627 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  8. Bierwirth, C., Meisel, F.: A follow-up survey of berth allocation and quay crane scheduling problems in container terminals. Eur. J. Oper. Res. 244(3), 675–689 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  9. Carlo, H.J., Vis, I.F.A., Roodbergen, K.J.: Storage yard operations in container terminals: Literature overview, trends, and research directions. Eur. J. Oper. Res. 235(2), 412–430 (2014) (Maritime Logistics)

    Google Scholar 

  10. Carlo, H.J., Vis, I.F.A., Roodbergen, K.J.: Transport operations in container terminals: Literature overview, trends, research directions and classification scheme. Eur. J. Oper. Res. 236(1), 1–13 (2014)

    Article  MATH  Google Scholar 

  11. Caserta, M., Schwarze, S., Vo\(\beta \), S.: A new binary description of the blocks relocation problem and benefits in a look ahead heuristic. In: Cotta, C., Cowling, P. (eds.) Evolutionary Computation in Combinatorial Optimization. Lecture Notes in Computer Science, vol. 5482, pp. 37–48. Springer, Berlin (2009)

    Google Scholar 

  12. Caserta, M., Schwarze, S., Vo\(\beta \), S.: Container rehandling at maritime container terminals. In: Böse, J.W., Sharda, R., Vo\(\beta \), S. (eds.) Handbook of Terminal Planning. Volume 49 of Operations Research/Computer Science Interfaces Series, pp. 247–269. Springer, New York (2011)

    Google Scholar 

  13. Caserta, M., Schwarze, S.: Vo\(\beta \), S.: A mathematical formulation and complexity considerations for the blocks relocation problem. Eur. J. Oper. Res. 219(1), 96–104 (2012)

    Article  MATH  Google Scholar 

  14. Caserta, M., Vo\(\beta \), S.: A corridor method-based algorithm for the pre-marshalling problem. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., Machado, P. (eds.) Applications of Evolutionary Computing. Volume 5484 of Lecture Notes in Computer Science, pp. 788–797. Springer, Berlin (2009)

    Google Scholar 

  15. Caserta, M.: Vo\(\beta \), S., Sniedovich, M.: Applying the corridor method to a blocks relocation problem. OR Spectr. 33(4), 915–929 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  16. Casey, B., Kozan, E.: Optimising container storage processes at multimodal terminals. J. Oper. Res. Soc. 63, 1126–1142 (2012)

    Article  Google Scholar 

  17. Chen, G., Govindan, K., Yang, Z.: Managing truck arrivals with time windows to alleviate gate congestion at container terminals. Int. J. Prod. Econ. 141(1), 179–188 (2013)

    Article  Google Scholar 

  18. Christensen, J., Pacino, D.: A matheuristic for the cargo mix problem with block stowage. Transp. Res. Part E: Logist. Transp. Rev. 97, 151–171 (2017)

    Google Scholar 

  19. Christiansen, M., Fagerholt, K., Nygreen, B., Ronen, D.: Ship routing and scheduling in the new millennium. Eur. J. Oper. Res. 228(3), 467–483 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  20. Christiansen, M., Fagerholt, K., Ronen, D.: Ship routing and scheduling: status and perspectives. Transp. Sci. 38(1), 1–18 (2004)

    Article  Google Scholar 

  21. Chung, S.H., Chan, F.T.S.: A workload balancing genetic algorithm for the quay crane scheduling problem. Int. J. Prod. Res. 51(16), 4820–4834 (2013)

    Article  Google Scholar 

  22. Chung, S.H., Choy, K.L.: A modified genetic algorithm for quay crane scheduling operations. Expert Syst. Appl. 39(4), 4213–4221 (2012)

    Article  Google Scholar 

  23. de Azevedo, A.T., Ribeiro, C.M., de Sena, G.J., Chaves, A.A., Neto, L.L.S., Moretti, A.C.: Solving the 3d container ship loading planning problem by representation by rules and meta-heuristics. Int. J. Data Anal. Tech. Strateg. 6(3), 228–260 (2014)

    Article  Google Scholar 

  24. de Castillo, B., Daganzo, C.F.: Handling strategies for import containers at marine terminals. Transp. Res. Part B: Methodol. 27(2), 151–166 (1993)

    Article  Google Scholar 

  25. Deo, M.C., Jha, A., Chaphekar, A.S., Ravikant, K.: Neural networks for wave forecasting. Ocean Eng. 28(7), 889–898 (2001)

    Article  Google Scholar 

  26. Dubrovsky, O., Levitin, G., Penn, M.: A genetic algorithm with a compact solution encoding for the container ship stowage problem. J. Heuristics 8(6), 585–599 (2002)

    Article  Google Scholar 

  27. Expósito-Izquierdo, C., González-Velarde, J.L., Melián-Batista, B., Moreno-Vega, J.M.: Hybrid estimation of distribution algorithm for the quay crane scheduling problem. Appl. Soft Comput. 13(10), 4063–4076 (2013)

    Article  Google Scholar 

  28. Expósito-Izquierdo, C., Lalla-Ruiz, E., de Armas, J., Melián-Batista, B., Moreno-Vega, J.M.: A heuristic algorithm based on an improvement strategy to exploit idle time periods for the stacking problem. Comput. Ind. Eng. 87, 410–424 (2015)

    Article  Google Scholar 

  29. Expósito-Izquierdo, C., Melián-Batista, B., Moreno-Vega, J.M.: Pre-marshalling problem: heuristic solution method and instances generator. Expert Syst. Appl. 39(9), 8337–8349 (2012)

    Article  Google Scholar 

  30. Expósito-Izquierdo, C., Melián-Batista, B., Moreno-Vega, J.M.: A domain-specific knowledge-based heuristic for the blocks relocation problem. Adv. Eng. Inf. 28(4), 327–343 (2014)

    Article  Google Scholar 

  31. Expósito-Izquierdo, C., Melián-Batista, B., Moreno-Vega, J.M.: An exact approach for the blocks relocation problem. Expert Syst. Appl. 42(1718), 6408–6422 (2015)

    Article  Google Scholar 

  32. 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: Computational Intelligence, pp. 323–343. Springer (2016)

    Google Scholar 

  33. Feo, T.A., Resende, M.G.C.: A probabilistic heuristic for a computationally difficult set covering problem. Oper. Res. Lett. 8(2), 67–71 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  34. Forster, F., Bortfeldt, A.: A tree search procedure for the container relocation problem. Comput. Oper. Res. 39(2), 299–309 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  35. Fransoo, J.C., Lee, C.Y.: The critical role of ocean container transport in global supply chain performance. Prod. Oper. Manag. 22(2), 253–268 (2013)

    Article  Google Scholar 

  36. Gharehgozli, A.H., Yu, Y., de Koster, R., Udding, J.T.: An exact method for scheduling a yard crane. Eur. J. Oper. Res. 235(2), 431–447 (2014) (Maritime Logistics)

    Google Scholar 

  37. Gheith, M., Eltawil, A.B., Harraz, N.A.: Solving the container pre-marshalling problem using variable length genetic algorithms. Eng. Optim. 48(4), 687–705 (2016)

    Article  Google Scholar 

  38. Glover, F.: Future paths for integer programming and links to artificial intelligence. Comput. Oper. Res. 13(5), 533–549 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  39. Gucma, L., Pietrzykowski, Z.: Ship manoeuvring in restricted areas: an attempt to quantify dangerous situations using a probabilistic-fuzzy method. J. Navig. 59(02), 251–262 (2006)

    Article  Google Scholar 

  40. Gunter, H.O., Kim, K.H.: Container terminals and terminal operations. OR Spectr. 28, 437–445 (2006)

    Article  Google Scholar 

  41. Hakam, M.H., Solvang, W.D., Hammervoll, T.: A genetic algorithm approach for quay crane scheduling with non-interference constraints at narvik container terminal. Int. J. Logist. Res. Appl. 15(4), 269–281 (2012)

    Article  Google Scholar 

  42. He, J., Huang, Y., Yan, W.: Yard crane scheduling in a container terminal for the trade-off between efficiency and energy consumption. Adv. Eng. Inf. 29(1), 59–75 (2015)

    Article  Google Scholar 

  43. Henesey, L.: Overview of transshipment operations and simulation. In: MedTrade Conference, Malta, pp. 6–7 (2006)

    Google Scholar 

  44. Holland, J.H.: Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press (1975)

    Google Scholar 

  45. Hosseini, S., Barker, K.: Modeling infrastructure resilience using bayesian networks: a case study of inland waterway ports. Comput. Ind. Eng. 93, 252–266 (2016)

    Article  Google Scholar 

  46. Hottung, A., Tierney, K.: A biased random-key genetic algorithm for the container pre-marshalling problem. Comput. Oper. Res. 75, 83–102 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  47. Iglesias, G., Rabuñal, J., Losada, M.A., Pachón, H., Castro, A., Carballo, R.: A virtual laboratory for stability tests of rubble-mound breakwaters. Ocean Eng. 35(1112), 1113–1120 (2008)

    Article  Google Scholar 

  48. Imai, A., Nagaiwa, K., Tat, C.W.: Efficient planning of berth allocation for container terminals in Asia. J. Adv. Transp. 31(1), 75–94 (1997)

    Article  Google Scholar 

  49. Imai, A., Sasaki, K., Nishimura, E., Papadimitriou, S.: Multi-objective simultaneous stowage and load planning for a container ship with container rehandle in yard stacks. Eur. J. Oper. Res. 171(2), 373–389 (2006)

    Article  MATH  Google Scholar 

  50. Kim, D.H., Kim, Y.J., Hur, D.S.: Artificial neural network based breakwater damage estimation considering tidal level variation. Ocean Eng. 87, 185–190 (2014)

    Article  Google Scholar 

  51. Kim, K.H.: Evaluation of the number of rehandles in container yards. Comput. Ind. Eng. 32(4), 701–711 (1997)

    Article  Google Scholar 

  52. Kim, K.H., Hong, G.P.: A heuristic rule for relocating blocks. Comput. Oper. Res. 33(4), 940–954 (2006)

    Article  MATH  Google Scholar 

  53. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  54. Korsvik, J.E., Fagerholt, K.: A tabu search heuristic for ship routing and scheduling with flexible cargo quantities. J. Heuristics 16(2), 117–137 (2010)

    Article  MATH  Google Scholar 

  55. Kosmas, O.T., Vlachos, D.S.: Simulated annealing for optimal ship routing. Comput. Oper. Res. 39(3), 576–581 (2012)

    Article  Google Scholar 

  56. Lalla-Ruiz, E., Expósito-Izquierdo, C., Melián-Batista, B., Moreno-Vega, J.M.: A set-partitioning-based model for the berth allocation problem under time-dependent limitations. Eur. J. Oper. Res. 250(3), 1001–1012 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  57. Lalla-Ruiz, E., González-Velarde, J.L., Melián-Batista, B., Moreno-Vega, J.M.: Biased random key genetic algorithm for the tactical berth allocation problem. Appl. Soft Comput. 22, 60–76 (2014)

    Article  Google Scholar 

  58. Lalla-Ruiz, E., Melián-Batista, B., Moreno-Vega, J.M.: Artificial intelligence hybrid heuristic based on tabu search for the dynamic berth allocation problem. Eng. Appl. Artif. Intell. 25(6), 1132–1141 (2012)

    Article  Google Scholar 

  59. Lalla-Ruiz, E., Voß, S., Expósito-Izquierdo, C., Melián-Batista, B., Moreno-Vega, J.M.: A popmusic-based approach for the berth allocation problem under time-dependent limitations. Ann. Oper. Res. 1–27 (2015)

    Google Scholar 

  60. Lee, Y., Chao, S.L.: A neighborhood search heuristic for pre-marshalling export containers. Eur. J. Oper. Res. 196(2), 468–475 (2009)

    Article  Google Scholar 

  61. Lee, Y., Lee, Y.J.: A heuristic for retrieving containers from a yard. Comput. Oper. Res. 37(6), 1139–1147 (2010)

    Article  MATH  Google Scholar 

  62. Legato, P., Trunfio, R., Meisel, F.: Modeling and solving rich quay crane scheduling problems. Comput. Oper. Res. 39(9), 2063–2078 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  63. Li, W., Wu, Y., Petering, M.E.H., Goh, M., de Souza, R.: Discrete time model and algorithms for container yard crane scheduling. Eur. J. Oper. Res. 198(1), 165–172 (2009)

    Article  MATH  Google Scholar 

  64. Liu, M., Zheng, F., Li, J.: Scheduling small number of quay cranes with non-interference constraint. Optim. Lett. 9(2), 403–412 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  65. Londhe, S.N., Deo, M.C.: Wave tranquility studies using neural networks. Marine Struct. 16(6), 419–436 (2003)

    Article  Google Scholar 

  66. López, I., López, M., Iglesias, G.: Artificial neural networks applied to port operability assessment. Ocean Eng. 109, 298–308 (2015)

    Article  Google Scholar 

  67. López, M., Iglesias, G.: Artificial intelligence for estimating infragravity energy in a harbour. Ocean Eng. 57, 56–63 (2013)

    Article  Google Scholar 

  68. Lu, Z., Han, X., Xi, L., Erera, A.L.: A heuristic for the quay crane scheduling problem based on contiguous bay crane operations. Comput. Oper. Res. 39(12), 2915–2928 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  69. McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115–133 (1943)

    Article  MathSciNet  MATH  Google Scholar 

  70. Meisel, F.: Operational planning problems. Seaside Operations Planning in Container Terminals. Contributions to Management Science, pp. 17–30. Physica-Verlag, HD (2009)

    Google Scholar 

  71. Meisel, F.: Seaside Operations Planning in Container Terminals. Springer (2009)

    Google Scholar 

  72. Meisel, F., Bierwirth, C.: A framework for integrated berth allocation and crane operations planning in seaport container terminals. Transp. Sci. 47(2), 131–147 (2013)

    Article  Google Scholar 

  73. Mladenović, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24(11), 1097–1100 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  74. Monaco, M.F., Sammarra, M., Sorrentino, G.: The terminal-oriented ship stowage planning problem. Eur. J. Oper. Res. 239(1), 256–265 (2014)

    Article  MATH  Google Scholar 

  75. Moon, I.K., Qiu, Z.B., Wang, J.H.: A combined tramp ship routing, fleet deployment, and network design problem. Marit. Policy Manag. 42(1), 68–91 (2015)

    Article  Google Scholar 

  76. Narasimhan, A., Palekar, U.S.: Analysis and algorithms for the transtainer routing problem in container port operations. Transp. Sci. 36(1), 63–78 (2002)

    Article  MATH  Google Scholar 

  77. Ng, W.C.: Crane scheduling in container yards with inter-crane interference. European Journal of Operational Research 164(1), 64–78 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  78. Pacino, D., Delgado, A., Jensen, R.M., Bebbington, T.: Fast generation of near-optimal plans for eco-efficient stowage of large container vessels. In: International Conference on Computational Logistics, pp. 286–301. Springer (2011)

    Google Scholar 

  79. Park, T.K., Kim, K.H.: Comparing handling and space costs for various types of stacking methods. Comput. Ind. Eng. 58(3), 501–508 (2010)

    Article  Google Scholar 

  80. Parreño, F., Pacino, D., Álvarez-Valdés, R.: A grasp algorithm for the container stowage slot planning problem. Transp. Res. Part E: Logist. Transp. Rev. 94, 141–157 (2016) (cited By 0)

    Google Scholar 

  81. Rei, R.J., Pedroso, J.P.: Heuristic search for the stacking problem. Int. Trans. Oper. Res. 19(3), 379–395 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  82. Romero, G., Durán, G., Marenco, J., Weintraub, A.: An approach for efficient ship routing. Int. Trans. Oper. Res. 20(6), 767–794 (2013)

    MathSciNet  MATH  Google Scholar 

  83. Ropke, S., Pisinger, D.: An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transp. Sci. 40(4), 455–472 (2006)

    Article  Google Scholar 

  84. Santhanakrishnan, S., Narendran, T.T., Ganesh, K., Anbuudayasankar, S.P.: Comparison of meta-heuristics for container ship routing problem. Int. J. Serv. Oper. Manag. 12(3), 348–367 (2012)

    Google Scholar 

  85. Shaw, P.: Using constraint programming and local search methods to solve vehicle routing problems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 1520, 417–431 (1998)

    Google Scholar 

  86. Smoczek, J.: Fuzzy crane control with sensorless payload deflection feedback for vibration reduction. Mech. Syst. Signal Process. 46(1), 70–81 (2014)

    Article  Google Scholar 

  87. Sörensen, K., Glover, F.: Metaheuristics. In: Gass, S., Fu, M. (eds.) Encyclopedia of Operations Research and Management Science, 3rd edn., pp. 960–970. Springer Science & Business Media (2013)

    Google Scholar 

  88. Sutrisnowati, R.A., Bae, H., Song, M.: Bayesian network construction from event log for lateness analysis in port logistics. Comput. Ind. Eng. 89, 53–66 (2015) (Maritime logistics and transportation intelligence)

    Google Scholar 

  89. Tagliaferri, F., Viola, I.M., Flay, R.G.J.: Wind direction forecasting with artificial neural networks and support vector machines. Ocean Engineering 97, 65–73 (2015)

    Article  Google Scholar 

  90. Tang, L., Zhao, J., Liu, J.: Modeling and solution of the joint quay crane and truck scheduling problem. Eur. J. Oper. Res. 236(3), 978–990 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  91. Tierney, K., Pacino, D., Vo\(\beta \), S.: Solving the pre-marshalling problem to optimality with a\(^*\) and ida\(^*\). Flex. Serv. Manuf. J. 1–37 (2016)

    Google Scholar 

  92. Tus, A., Rendl, A., Raidl, G.R.: Metaheuristics for the two-dimensional container pre-marshalling problem. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8994, 186–201 (2015)

    Google Scholar 

  93. Verdegay, J.L., Yager, R.R., Bonissone, P.P.: On heuristics as a fundamental constituent of soft computing. Fuzzy Sets Syst. 159(7), 846–855 (2008)

    Article  MathSciNet  Google Scholar 

  94. Vis, I.F.A., Carlo, H.J.: Sequencing two cooperating automated stacking cranes in a container terminal. Transp. Sci. 44(2), 169–182 (2010)

    Article  Google Scholar 

  95. Vis, I.F.A., van Anholt, R.G.: Performance analysis of berth configurations at container terminals. OR Spectr. 32(3), 453–476 (2010)

    Article  Google Scholar 

  96. Wang, S., Zheng, J., Zheng, K., Guo, J., Liu, X.: Multi resource scheduling problem based on an improved discrete particle swarm optimization. Phys. Proc. 25, 576–582 (2012)

    Article  Google Scholar 

  97. Wilson, I.D., Roach, P.A.: Principles of combinatorial optimization applied to container-ship stowage planning. J. Heuristics 5(4), 403–418 (1999)

    Article  MATH  Google Scholar 

  98. Xie, G., Wang, S., Zhao, Y., Lai, K.K.: Hybrid approaches based on LSSVR model for container throughput forecasting: a comparative study. Appl. Soft Comput. 13(5), 2232–2241 (2013)

    Article  Google Scholar 

  99. Yan, J., Sun, X., Liu, J.J.: Assessing container operator efficiency with heterogeneous and time-varying production frontiers. Transp. Res. Part B: Methodol. 43(1), 172–185 (2009)

    Article  Google Scholar 

  100. Yeo, G.T., Ng, A.K.Y., Lee, P.T.W., Yang, Z.: Modelling port choice in an uncertain environment. Marit. Policy Manag. 41(3), 251–267 (2014)

    Article  Google Scholar 

  101. Zadeh, L.A.: Fuzzy logic (abstract): Issues, contentions and perspectives. In: Proceedings of the 22nd Annual ACM Computer Science Conference on Scaling Up: Meeting the Challenge of Complexity in Real-world Computing Applications: Meeting the Challenge of Complexity in Real-world Computing Applications, CSC ’94, New York, NY, USA, p. 407. ACM (1994)

    Google Scholar 

  102. Zhang, Z., Lee, C.Y.: Multiobjective approaches for the ship stowage planning problem considering ship stability and container rehandles. IEEE Trans. Syst. Man Cybern.: Syst. 46(10), 1374–1389 (2016)

    Google Scholar 

  103. Zhao, N., Shen, Y., Xia, M., Jin, J.: A novel strategy for stowage planning of 40 feet containers in container terminals. J. Marine Sci. Technol. (Taiwan) 24(1), 61–74 (2016)

    Google Scholar 

Download references

Acknowledgements

This work has been partially funded by the Spanish Ministry of Economy and Competitiveness (project TIN2015-70226-R) with FEDER funds.

Author information

Authors and Affiliations

  1. Universidad de La Laguna, Santa Cruz de Tenerife, Spain

    Christopher Expósito-Izquierdo, Belén Melián-Batista & J. Marcos Moreno-Vega

Authors
  1. Christopher Expósito-Izquierdo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Belén Melián-Batista
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. Marcos Moreno-Vega
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christopher Expósito-Izquierdo .

Editor information

Editors and Affiliations

  1. Department of Computer Science and A.I. Higher Technical School of Information Technology and Telecommunications Engineering, University of Granada, Granada, Spain

    David A. Pelta

  2. Department of Computer Science and A.I. Higher Technical School of Information Technology and Telecommunications Engineering, University of Granada , Granada, Spain

    Carlos Cruz Corona

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Expósito-Izquierdo, C., Melián-Batista, B., Moreno-Vega, J.M. (2018). A Review of Soft Computing Techniques in Maritime Logistics and Its Related Fields. In: Pelta, D., Cruz Corona, C. (eds) Soft Computing Based Optimization and Decision Models. Studies in Fuzziness and Soft Computing, vol 360. Springer, Cham. https://doi.org/10.1007/978-3-319-64286-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-64286-4_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64285-7

  • Online ISBN: 978-3-319-64286-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation