A simulation–optimization framework for enhancing robustness in bulk berth scheduling

Alan Dávila de León, Eduardo Lalla-Ruiz*, Belén Melián-Batista, J. Marcos Moreno-Vega

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)
31 Downloads (Pure)


The service time of the vessels is one of the main indicators of ports’ competitiveness. This, together with the increasing volume of bulk transportation, make the efficient management of scarce resources such as berths a crucial option for enhancing the productivity of the overall terminal. In real scenarios, the information available to port operators may vary once the planning has been elaborated. Unforeseen events, errors, or modifications in the available information can lead to inefficient terminal management and the initial scheduling might become unfeasible. This implies that the use of deterministic approaches may not be enough to maximize productivity. Therefore, in this work, proactive simulation–optimization approaches that utilize the information collected during the simulation for guiding the optimization search to provide robust solutions are proposed. Moreover, a multi-objective approach based on the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for jointly tackling the problem objective as well as the deviations because of stochastic changes is developed. Finally, we also investigate the contribution of time management strategies such as buffers to absorb stochastic modifications and hence increase solutions’ robustness. The computational results indicate, on the one hand, the benefit of integrating both types of objectives (i.e., deterministic and stochastic) to guide the simulation–optimization process, and on the other hand, the benefit of using the multi-objective approaches like NSGA-II. Finally, the incorporation of buffers leads to better performance in terms of reducing penalty costs due to disruptions, shortening the planning risks related to only considering deterministic planning.

Original languageEnglish
Article number104276
JournalEngineering applications of artificial intelligence
Early online date18 May 2021
Publication statusPublished - Aug 2021


  • Berth Allocation Problem
  • Buffer management
  • Maritime logistics
  • Metaheuristics
  • Simheuristics
  • Simulation–optimization
  • operations research
  • Logistics
  • optimization
  • UT-Hybrid-D

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