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Optimizing the Crane’s Operating Time with the Ant Colony Optimization and Pilot Method Metaheuristics

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Frontier Applications of Nature Inspired Computation

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

Abstract

The container retrieval problem (CRP) is a practical problem, as it enables high operational efficiency in a container terminal system. The CRP involves finding an optimal sequence of operations for the crane, making it possible to retrieve all the containers from the bay according to a predefined order. An optimal sequence of operations is obtained by reducing the operating time of the crane. Although reducing the number of container relocations is the primary optimization goal currently discussed in the literature, minimizing the crane’s operating time is best suited for assessing the operational efficiency of the crane. Besides, as it has been already reported in previous studies, minimizing the number of relocations does not ensure the solution with the minimum operating time. In addition, little attention has been devoted to the investigation of adopting ant colony optimization (ACO) to minimize the crane’s operating time. Therefore, this study proposes a heuristic algorithm, an ACO algorithm, and a pilot method algorithm for the CRP, with a focus on minimizing the crane’s operating time. The computational experiments show that the proposed algorithms have produced better solutions in comparison with leading algorithms from the literature.

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Notes

  1. 1.

    The 1200 instances are available at www.cin.ufpe.br/~asf2/csp/instances/.

References

  1. Azari E (2015) Notes on “a mathematical formulation and complexity considerations for the blocks relocation problem”. Scientia Iranica 22(6):2722–2728

    Google Scholar 

  2. Bacci T, Mattia S, Ventura P (2019) The bounded beam search algorithm for the block relocation problem. Comput Oper Res 103:252–264. https://doi.org/10.1016/J.COR.2018.11.008

    Article  MathSciNet  MATH  Google Scholar 

  3. Carlo HJ, Vis IF, Roodbergen KJ (2014) Storage yard operations in container terminals: literature overview, trends, and research directions. Eur J Oper Res 235(2):412–430. https://doi.org/10.1016/j.ejor.2013.10.054

    Article  MATH  Google Scholar 

  4. Caserta M, Schwarze S, Voß S (2012) A mathematical formulation and complexity considerations for the blocks relocation problem. Eur J Oper Res 219(1):96–104. https://doi.org/10.1016/j.ejor.2011.12.039

    Article  MathSciNet  MATH  Google Scholar 

  5. Dey N (ed) (2018) Advancements in applied metaheuristic computing. Advances in data mining and database management. IGI Global. https://doi.org/10.4018/978-1-5225-4151-6

  6. Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66. https://doi.org/10.1109/4235.585892

    Article  Google Scholar 

  7. Feillet D, Parragh SN, Tricoire F (2019) A local-search based heuristic for the unrestricted block relocation problem. Comput Oper Res 108:44–56. https://doi.org/10.1016/J.COR.2019.04.006

    Article  MathSciNet  MATH  Google Scholar 

  8. Forster F, Bortfeldt A (2012) A tree search heuristic for the container retrieval problem. In: Klatte D, Lüthi HJ, Schmedders K (eds) Operations research proceedings 2011 SE-41, operations research proceedings. Springer Berlin Heidelberg, pp 257–262. https://doi.org/10.1007/978-3-642-29210-1_41

  9. Galle V, Barnhart C, Jaillet P (2018) A new binary formulation of the restricted container relocation problem based on a binary encoding of configurations. Eur J Oper Res 267(2):467–477. https://doi.org/10.1016/j.ejor.2017.11.053

    Article  MathSciNet  MATH  Google Scholar 

  10. Gupta N, Khosravy M, Patel N, Sethi I (2018) Evolutionary optimization based on biological evolution in plants. Proc Comput Sci 126:146–155. https://doi.org/10.1016/j.procs.2018.07.218

    Article  Google Scholar 

  11. Hussein M, Petering MEH (2012) Genetic algorithm-based simulation optimization of stacking algorithms for yard cranes to reduce fuel consumption at seaport container transshipment terminals. In: 2012 IEEE congress on evolutionary computation (CEC). IEEE, pp 1–8. https://doi.org/10.1109/CEC.2012.6256471

  12. Inaoka Y, Tanaka S (2017) A branch-and-bound algorithm for the block relocation problem to minimize total crane operation time. In: 19th international conference on harbor maritime and multimodal logistics M&S (HMS 2017), pp 98–104

    Google Scholar 

  13. Jin B, Zhu W, Lim A (2015) Solving the container relocation problem by an improved greedy look-ahead heuristic. Eur J Oper Res 240(3):837–847. https://doi.org/10.1016/j.ejor.2014.07.038

    Article  MathSciNet  MATH  Google Scholar 

  14. Jovanovic R, Tuba M, Voß S (2019) An efficient ant colony optimization algorithm for the blocks relocation problem. Eur J Oper Res 274(1):78–90. https://doi.org/10.1016/J.EJOR.2018.09.038

    Article  MathSciNet  MATH  Google Scholar 

  15. Jovanovic R, Voß S (2014) A chain heuristic for the blocks relocation problem. Comput Ind Eng 75:79–86. https://doi.org/10.1016/j.cie.2014.06.010

    Article  Google Scholar 

  16. Khosravy M, Gupta N, Patel N, Senjyu T, Duque CA (2019) Particle swarm optimization of morphological filters for electrocardiogram baseline drift estimation. Springer, pp 1–21. https://doi.org/10.1007/978-981-13-9263-4_1

  17. Kim Y, Kim T, Lee H (2016) Heuristic algorithm for retrieving containers. Comput Ind Eng. https://doi.org/10.1016/j.cie.2016.08.022

    Article  Google Scholar 

  18. Ku D, Arthanari TS (2016) On the abstraction method for the container relocation problem. Comput Oper Res 68:110–122. https://doi.org/10.1016/j.cor.2015.11.006

    Article  MathSciNet  MATH  Google Scholar 

  19. Lee Y, Lee YJ (2010) A heuristic for retrieving containers from a yard. Comput Oper Res 37(6):1139–1147. https://doi.org/10.1016/j.cor.2009.10.005

    Article  MATH  Google Scholar 

  20. Lin DY, Lee YJ, Lee Y (2015) The container retrieval problem with respect to relocation. Transp Res Part C Emerg Technol 52:132–143. https://doi.org/10.1016/j.trc.2015.01.024

    Article  Google Scholar 

  21. Moraes CA, De Oliveira EJ, Khosravy M, Oliveira LW, Honório LM, Pinto MF (2019) A hybrid bat-inspired algorithm for power transmission expansion planning on a practical Brazilian network. Springer, pp 71–95.https://doi.org/10.1007/978-981-13-9263-4_4

  22. Murty KG, Liu J, Wan YW, Linn R (2005) A decision support system for operations in a container terminal. Decis Support Syst 39(3):309–332. https://doi.org/10.1016/j.dss.2003.11.002

  23. Quispe KEY, Lintzmayer CN, Xavier EC (2018) An exact algorithm for the blocks relocation problem with new lower bounds. Comput Oper Res 99:206–217. https://doi.org/10.1016/J.COR.2018.06.021

    Article  MathSciNet  MATH  Google Scholar 

  24. Sheskin DJ (2007) Handbook of parametric and nonparametric statistical procedures, 4th edn. Chapman & Hall/CRC

    Google Scholar 

  25. de Melo da Silva M, Erdogan G, Battarra M, Strusevich V (2018) The block retrieval problem. Eur J Oper Res 265(3):931–950. https://doi.org/10.1016/j.ejor.2017.08.048

  26. da Silva Firmino A, de Abreu Silva RM, Times VC (2016) An exact approach for the container retrieval problem to reduce crane’s trajectory. In: 2016 IEEE 19th international conference on intelligent transportation systems (ITSC), pp 933–938. https://doi.org/10.1109/itsc.2016.7795667

  27. da Silva Firmino A, de Abreu Silva RM, Times VC (2019) A reactive grasp metaheuristic for the container retrieval problem to reduce crane’s working time. J Heurist 25(2):141–173. https://doi.org/10.1007/s10732-018-9390-0

    Article  Google Scholar 

  28. Tanaka S, Takii K (2016) A faster branch-and-bound algorithm for the block relocation problem. Autom Sci Eng 13(1):181–190. https://doi.org/10.1109/TASE.2015.2434417

    Article  Google Scholar 

  29. Ting CJ, Wu KC (2017) Optimizing container relocation operations at container yards with beam search. Transp Res Part E Logist Transp Rev 103:17–31. https://doi.org/10.1016/j.tre.2017.04.010

  30. Tricoire F, Scagnetti J, Beham A (2018) New insights on the block relocation problem. Comput Oper Res 89:127–139. https://doi.org/10.1016/J.COR.2017.08.010

    Article  MathSciNet  MATH  Google Scholar 

  31. UNCTAD: Review of Maritime Transport (2017). UNITED NATIONS PUBLICATION. https://www.unctad.org/en/PublicationsLibrary/rmt2017_en.pdf

  32. Ünlüyurt T, Aydin C (2012) Improved rehandling strategies for the container retrieval process. J Adv Transp 46(4):378–393. https://doi.org/10.1002/atr.1193

    Article  Google Scholar 

  33. Voß S, Fink A, Duin C (2005) Looking ahead with the pilot method. Annals Oper Res 136(1):285–302. https://doi.org/10.1007/s10479-005-2060-2

  34. World Bank: container port traffic (teu: 20 foot equivalent units) (2018). https://www.data.worldbank.org/indicator/IS.SHP.GOOD.TU?end=2018&start=2000. Accessed 30 Aug 2019

  35. World Cargo News: GCS adds RMG at Moscow terminal (2013). www.worldcargonews.com/news/news/gcs-adds-rmg-at-moscow-terminal-32292. Accessed 30 Aug 2019

  36. Zehendner E, Caserta M, Feillet D, Schwarze S, Voß S (2015) An improved mathematical formulation for the blocks relocation problem. Eur J Oper Res 245(2):415–422. https://doi.org/10.1016/j.ejor.2015.03.032

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Center for Informatics, Federal University of Pernambuco, Recife, PE, Brazil

    Andresson da Silva Firmino, Valéria Cesário Times & Ricardo Martins de Abreu Silva

Authors
  1. Andresson da Silva Firmino
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  2. Valéria Cesário Times
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  3. Ricardo Martins de Abreu Silva
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Corresponding author

Correspondence to Andresson da Silva Firmino .

Editor information

Editors and Affiliations

  1. Media Integrated Communication Lab, Graduate School of Engineering, Osaka University, Osaka, Japan

    Mahdi Khosravy

  2. Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA

    Neeraj Gupta

  3. Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA

    Nilesh Patel

  4. Department of Electrical and Electronics Engineering, Faculty of Engineering, University of the Ryukyus, Nishihara, Japan

    Tomonobu Senjyu

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da Silva Firmino, A., Times, V.C., de Abreu Silva, R.M. (2020). Optimizing the Crane’s Operating Time with the Ant Colony Optimization and Pilot Method Metaheuristics. In: Khosravy, M., Gupta, N., Patel, N., Senjyu, T. (eds) Frontier Applications of Nature Inspired Computation. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-2133-1_17

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