Skip to main content

Towards a Matheuristic Approach for the Berth Allocation Problem

  • Conference paper
  • First Online:
Learning and Intelligent Optimization (LION 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8426))

Included in the following conference series:

Abstract

The Berth Allocation Problem aims at assigning and scheduling incoming vessels to berthing positions along the quay of a container terminal. This problem is a well-known optimization problem within maritime shipping. For solving it, we propose two POPMUSIC (Partial Optimization Metaheuristic Under Special Intensification Conditions) approaches that incorporate an existing mathematical programming formulation. POPMUSIC is an efficient metaheuristic that may serve as blueprint for matheuristics approaches once hybridized with mathematical programming. In this regard, the use of exact methods for solving the sub-problems defined in the POPMUSIC template highlight an interoperation between metaheuristics and mathematical programming techniques, which provide a new type of approach for this problem. Computational experiments reveal excellent results.

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

Access this chapter

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 EPUB and 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

Institutional subscriptions

References

  1. 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  MATH  Google Scholar 

  2. Buhrkal, K., Zuglian, S., Ropke, S., Larsen, J., Lusby, R.: Models for the discrete berth allocation problem: a computational comparison. Transp. Res. Part E 47(4), 461–473 (2011)

    Article  Google Scholar 

  3. Christensen, C.G., Holst, C.T.: Berth allocation in container terminals. Master’s thesis, Technical University of Denmark (2008)

    Google Scholar 

  4. Cordeau, J.F., Laporte, G., Legato, P., Moccia, L.: Models and tabu search heuristics for the berth-allocation problem. Transp. Sci. 39, 526–538 (2005)

    Article  Google Scholar 

  5. de Oliveira, R.M., Mauri, G.R., Lorena, L.A.N.: Clustering search for the berth allocation problem. Expert Syst. Appl. 39(5), 5499–5505 (2012)

    Article  Google Scholar 

  6. 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 

  7. Sniedovich, M., Voß, S.: The corridor method: a dynamic programming inspired metaheuristic. Control Cybern. 35(3), 551–578 (2006)

    MATH  Google Scholar 

  8. Ching-Jung, T., Kun-Chih, W., Hao, C.: Particle swarm optimization algorithm for the berth allocation problem. Expert Syst. Appl. 41, 1543–1550 (2014)

    Article  Google Scholar 

  9. Taillard, É., Voß, S.: POPMUSIC - partial optimization metaheuristic under special intensification conditions. In: Ribeiro, C.C., Hansen, P. (eds.) Essays and Surveys in Metaheuristics, pp. 613–629. Kluwer, Boston (2002)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Eduardo Aníbal Lalla-Ruiz .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Lalla-Ruiz, E.A., Voß, S. (2014). Towards a Matheuristic Approach for the Berth Allocation Problem. In: Pardalos, P., Resende, M., Vogiatzis, C., Walteros, J. (eds) Learning and Intelligent Optimization. LION 2014. Lecture Notes in Computer Science(), vol 8426. Springer, Cham. https://doi.org/10.1007/978-3-319-09584-4_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-09584-4_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09583-7

  • Online ISBN: 978-3-319-09584-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics