Container Handling Strategy Dataset: Efficiency Analysis in Dockyard Operations

Citation Author(s):
Md Abrar
Md. Mahfuzur
Submitted by:
Md Abrar Jahin
Last updated:
Sun, 03/17/2024 - 13:10
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The datasets comprise configurations of loading and unloading plans for container ships, generated under six distinct scenarios based on varying numbers of stacks and maximum stack heights of containers in each row. Considering typical container ship characteristics, scenarios encompass stack numbers ranging from 5 to 30 and maximum stack heights from 4 to 10. The dataset includes loading and unloading plans for dockyard containers, with sample plans provided for small ships. Each of the 5 datasets comprises 20 instances representing different container loading and unloading scenarios. Each instance is characterized by a specific strategy detailing the number of single cycles, dual cycles, rehandles, and operation time required. The dataset offers insights into the efficiency and performance of various strategies in handling container logistics within a dockyard setting.


Dataset Overview:

  • Each of the 5 datasets consists of 20 instances, each representing a unique scenario in container handling operations.
  • Instances are labeled sequentially from 1 to 20, with corresponding details provided for each scenario.

Instance Details:

  • Each instance includes the following parameters:
    • Scenario: Numerical identifier for the specific scenario.
    • Instance: Numerical identifier for each instance within the scenario.
    • Strategy: Description of the strategy employed for container handling, denoted by a combination of characters.
    • No. of Single Cycles: Total number of single cycles involved in the operation.
    • No. of Dual Cycles: Total number of dual cycles involved in the operation.
    • No. of Rehandles: Number of times containers are rehandled during the operation.
    • Operation Time: Total duration of the operation, measured in seconds.


  • Researchers and logistics and container handling practitioners can utilize this dataset to analyze the effectiveness of different strategies in optimizing loading and unloading operations.
  • The dataset can be employed for benchmarking purposes, comparing the performance of various strategies and identifying areas for improvement in container logistics management.
  • Additionally, the dataset is a resource for developing and validating optimization algorithms and decision-support systems in container terminal operations.