MARITIME RESEARCH: Customized Maritime Analytics

Seaports that partner with a maritime education program can establish a fruitful relationship thatis beneficial for both sides, but finding the right fit takes research and persistence.

By Dr. ManWo Ng, Old Dominion University

When academia and industry work together, sometimes seemingly insur­mountable requisites can be achieved, such as the newest ones involving container transportation.

While trucking generally remains the predominant mode of transporting containers in and out of marine terminals, on-dock rail has steadily been gaining traction due to its distinct advantages, including environmental benefits and reduced terminal congestion. For example, the ports of Los Angeles and Long Beach recently received millions in grant funding from the U.S. Department of Transportation to improve rail access and on-dock rail capacity.

To maximize the benefits of on-dock rail, it is important for trains to leave the terminal fully packed. This is, however, not always an easy task as it depends on a number of factors, such as the containers available and the loading constraints. For example, one cannot place 20-foot containers above 40-foot containers. Loaded containers are typically not placed above empties. There are also weight restrictions for the rail cars.

Traditionally, to solve the puzzle of building fully packed trains, rail managers at a major container terminal in the U.S. we worked with, use their experience in combination with the terminal operating system (TOS), to decide where to place the containers on the rail cars. This usually works well – until new requirements emerge.

On top of all prior considerations, at this particular terminal, there was a new destination requirement to ensure that each outbound train consist of containers going to selected rail hubs. For example, while it was previously acceptable to have trains full of containers destined for Chicago only, now the train must carry containers to several other rail hubs, in addition to Chicago.

Because of this additional constraint, the existing TOS was no longer sufficient. It would also take a considerable amount of time to get it updated to reflect the new loading constraint.

That is where academia came in, and helped solve the equation. After spending hours with the rail manager to fully understand the rail loading processes, we developed a customized prescriptive analytics solution that incorporated the new requirement. Extensive testing of our solution showed that it was always possible to find loading plans that are the best possible for the available containers. Note that train space utilization depends on the rail cars, available containers, their sizes, weights, destinations etc. As a result, utilizations can sometimes be less than 100% since the “right” containers might not be available at the right time. In such scenarios, our solution was still able to find the best utilization possible given what was available.

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