A Constrained Fuzzy Knowledge-Based System for the Management of Container Yard Operations

Ali Abbas, Dr Ammar Al-Bazi*, Vasile Palade

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The management of container yard operations is considered by yard operators to be a very challenging task due to the many uncertainties inherent in such operations.
The storage of the containers is one of those operations that require proper management for the efficient utilisation of the yard, requiring rapid retrieval time and a minimum number of re-handlings. The main challenge is when containers of a different size, type, or weight need to be stored in a yard that holds a number of pre-existing containers. This challenge becomes even more complex when the date and time for the departure of the containers are unknown, as is the case when the container is collected by a third-party logistics company without any prior notice being given. The aim of this study is to develop a new system for the management of container yard operations
that takes into consideration a number of factors and constraints that occur in a real-life situation. One of these factors is the duration of stay for the topmost containers of each stack, when the containers are stored. Because the
duration of stay for containers in a yard varies dynamically over time, an ‘ON/OFF’ strategy is proposed to activate/deactivate the duration of stay factor constraint if the length of stay for these containers varies significantly over
time. A number of tools and techniques are utilised for developing the proposed system including: discrete event simulation for the modelling of container storage and retrieval operations, a fuzzy know ledge-based model for
the stack allocation of containers, and a heuristic algorithm called ‘neighbourhood’ for the container retrieval operation. Results show that by adopting the proposed ‘ON/OFF’ strategy, 5% of the number of re-handlings, 2.5% of the total retrieval time, 6.6% of the total re-handling time and
42% of the average waiting time per truck are reduced.
Original languageEnglish
Pages (from-to)1205-1223
Number of pages19
JournalInternational Journal of Fuzzy Systems
Early online date25 Jan 2018
Publication statusPublished - Apr 2018

Bibliographical note

Copyright © Springer Nature B.V. 2018. The final publication is available at Springer via https://doi.org/10.1007/s40815-018-0448-9


  • Constrained fuzzy knowledge-based system
  • 'ON/OFF' strategy
  • 'Neighbourhood' algorithm
  • Container yard operations


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