A hybrid estimation of distribution algorithm for a container pre-marshalling process


  • Ricardo Pérez-Rodríguez CONACYT - CIMAT
  • Arturo Hernández-Aguirre CIMAT
  • Sergio Nava-Muñoz CIMAT
  • Ignacio Méndez-Gómez-Humarán Researcher
  • Israel Manzano-Martinez Researcher




Evolutionary algorithms, optimization, pre-marshalling, seaport operations.


In a yard storage area, pre-marshaling means relocating the export containers into a proper arrangement in order to increase the efficiency of the load process. The container pre-marshaling process aims to identify the best sequence of movements of container movements on an initial layout of the yard storage area, so that it reaches a desired final layout and satisfies operational constraints, while minimizing the total number of movements required to achieve the best sequence. Contrary to current research, a new evolutionary algorithm is proposed and developed to solve the pre-marshaling problem and simulate the solution. Our approach combines the key advantages of both evolutionary algorithms and the Mallows model. The Mallows distribution is used to model the pre-marshaling scenario, while an evolutionary algorithm is used to guide the overall search process to identify the best performing sequences. The approach makes use of the Mallows model to describe the distribution of the solution space. The proposed algorithm is able to identify the next most probable movement in the yard storage area. General and standard benchmarking and real-world cases served as input and test parameters in order to show the performance of the proposed algorithm.

Author Biographies

Ricardo Pérez-Rodríguez, CONACYT - CIMAT


Arturo Hernández-Aguirre, CIMAT


Sergio Nava-Muñoz, CIMAT




How to Cite

Pérez-Rodríguez, R., Hernández-Aguirre, A., Nava-Muñoz, S., Méndez-Gómez-Humarán, I., & Manzano-Martinez, I. (2019). A hybrid estimation of distribution algorithm for a container pre-marshalling process. International Journal of Industrial Engineering: Theory, Applications and Practice, 26(3). https://doi.org/10.23055/ijietap.2019.26.3.3992



Logistics and Material Handling