Case studies

RUSCON is one of the major container shipping companies in the CIS region. The company needed a container yard planning solution to modernize one of the rail container terminal and determine maximum capacity of the whole facility and its components. It sought the expertise of container yard simulation specialists from Dilibrium Consulting Company to model terminal components including the container yard, transshipment equipment, and rail infrastructure.

Daltransugol coal terminal, located in the deep-water Muchka Bay, is the most important transshipment point on the way to the Asia-Pacific region. In order to increase its capacity to 40 million tons, Dilibrium conducted an in-depth analysis of all processes involved at the terminal using AnyLogic simulation.

Evans & Peck used AnyLogic software to simulate port berth capacity. The goal was to find out how much additional product could pass through the port without upgrading it.

The volume of cargo traffic in Russian ports quadrupled during the period from 2000 to 2016. The growth of cargo traffic led to the need to build new modern terminals. Another challenge, in this regard, was the choice between the creation of new terminals and the modernization of the existing ones. The managing company of Novorossiysk Sea Port planned to expand the existing trading terminal, so that, in the future, its capacity would increase to 1.2 million tons per year. For project implementation, the managing company hired an engineering company, Morstroytechnologiya, to devise maritime...

Evans & Peck was involved in multi-purpose harbour design planning. The company utilised a discrete event simulation modeling approach to map the transportation, warehousing, and loading to ships of product.

Wolverine Terminals wanted to launch a marine fueling service in a port in Canada but faced significant challenges—no historical data, complex logistics, and environmental constraints. Partnering with MOSIMTEC, they developed a discrete-event simulation in AnyLogic to optimize port efficiency and validate throughput capacity. A data-driven model helped to identify bottlenecks and achieve fuel supply optimization, ensuring smooth fuel distribution.