Multi-agent Optimization of the Intermodal Terminal Main Parameters: Research Based on a Case Study

Due to numerous uncertainties such as bad weather conditions, frequent changes in the schedules of vessels, breakdowns of equipment, port managers are aiming at providing adaptive and flexible strategic planning of their facilities, especially intermodal terminals (dry ports).

The problem is to optimize the long term physical and technical parameters of intermodal terminals and the parameters of traffic flows. These optimal values of parameters are characterized by low investments and sustainable social, economic and environmental impacts. One of the effective ways to investigate these impacts is a combination of analytical and simulation models.

Firstly, in order to provide the express assessment of the dry port construction project, the researchers have developed an agent-based system dynamics simulation model (ABSDS model), which optimizes the averaged values of main dry port parameters.

Secondly, the authors have developed an agent-based discrete event simulation model (ABDES model) of a seaport – dry port system. Basically, this model ensures the detailed estimation of financial indicators of a seaport – dry port system with the obtained optimal values of the intermodal terminal main parameters.

The research is based on the Ningbo-Zhoushan (China) port case study. The seaport is one of the busiest marine terminals in the world, handling approximately 25 mln TEUs annually. At a certain point it started experiencing lengthy delays and queues of vessels at anchorage waiting to enter the terminals for at least 7 days. As a result of a survey, one of the dry ports was identified as a bottleneck in the operation of the Ningbo-Zhoushan port.


Dynamics of the performance indicators of the seaport – dry port system before and after optimization
Dynamics of the performance indicators of the seaport – dry port system before and after optimization of the main dry port parameters: (a) container volume, (b) operational costs, (c) cost price of container handling

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