Simulation of barge transport systems

Barge transport accounts for more than $35 billion of US GDP and accounts for more than 300,000 jobs in US economy. Economic activity in barge transport logistics is forecasted to grow. This is due to several significant advantages of barge transport:

  • Congestion reduction, taking away traffic from road and rail systems
  • Cost efficient; cheap transportation mode especially for bulk material
  • Fuel efficiency and more environmentally friendly
  • Higher safety (e.g. lower mortality compared to other transportation modes)

In this article I explain how simulation can be used to improve economic results of barge transport systems. I highlight commercial and open source tools and discuss a custom barge transport simulation project that I developed in the past.

Characteristics and benefits of barge transport systems

Barge transports take place on river systems and primarily consist of a tugboat and several barges being transported by the boat. Boats and barges are transported between loading and unloading stations. There can also be distribution points, where boats e.g. exchange barges, are merged into larger groups, or split into to separate smaller barge groups. Below is a snapshot from Google Maps, taken from the Ohio River, USA. In the US, barge transports are primarily used in Louisiana, Texas, Kentucky, Florida, and New York. For example on the Mississippi River. But also in Europe barge transports play an important logistics role in industry. For example on the Rhine in Germany.

Barge transports are primarily used to facilitate cost efficient transport of bulk loads, such as grain, coal, cement, ores, scrap, and so on. According to the US National Waterways Foundation, 15 barges equal 216 rail cars, or 1,050 large semi-tractor trailers.

Simulation of barge transports for cost savings

The major challenge of barge transport systems is to decide on the amount and location of distribution points, and to decide on an optimal amount of tugboats and barges. The investment need for a barge is within the range $200,000 to $400,000, with the price of a tugboat being much higher than this. Due to the the large transport volumes of bulk material the real challenge is to ensure that enough empty barges are available for loading operations at loading points, and that they are delivered to unloading points with schedules delivery times.

In short, the major optimization levers for barge transport operations are:

  • Distribution points with regards to quantity, capacity and locations. Each loading point comes along with fixed setup costs and annual operational expenses. The advantage of distribution points is e.g. that smaller barge groups can be consolidated into larger groups, thereby increasing tugboat utilization and throughput per tugboat.
  • Amount of tugboats. The higher the amount of tugboats the hgiher the possible shipping frequency. The less boats the higher the average amount of barges transporter per tugboat.
  • Amount of barges. The more empty barges in the system, the easier it is to make sure that a sufficient amount of empty barges it available at the various loading points.
  • Dispatch policies and strategies that decide on job assignments, and assign tugboats to barges and their destinations.
  • Routing strategies decide on barge group routings through distribution points, which barge groups are to be split, regrouped, or merged.

A dynamic discrete-event simulation model can incorporate all of these optimization levers, while correctly reflecting system behaviours and dynamics – such as e.g. loading and production schedules for loading points, and order delivery schedules and demand patterns for unloading stations, and material flows.

Simulation tools for barge transport systems

There are many tools that can be used for simulation modeling of barge transport systems. One popular commercial tool with a broad user group is AnyLogic. However, AnyLogic does not have a specific barge transport simulation library. It is possible to model custom libraries with AnyLogic, and using this approach it is possible to implement a custom barge transport simulation library. This library can then be applied on specific barge transport systems.

The same, however, is also possible with free tools. For exampel SimPy and salabim in Python, or JaamSim. JaamSim has a drag-and-grop tool box modeling framework with queues, routings, event handling and so on. Using this approach it is possible to implement a barge transport process simulation without any coding. Using SimPy or salabim, on the other hand, requires programming and object-oriented programming experience in Python. This makes these tools more complex but also more comprehensive and flexible.

Case study application example in SimPy

I have developed a barge transport simulation model in SimPy. It is available in the SCDA shop. You can find a detailed description via the link below.

The simulation model implements a Mississippi River case study, in which barges are transported from a loading station to an unloading station. Both loading and unloading stations operation operate with defined processing speeds and durations. The simulation model tracks the position of boats and barges on the river, and reports the throughput maintained at loading and unloading stations, as well as the overall barge utilization. The graphs below summarize one simulatio run out of many that I conducted as part of a sensitivity analysis. The sensitivity analysis ran the river simulation with a varying amounts of tugboats and barges.

The color legend shows the tugboat ID numbers, i.e. red is tugboat with ID #3.

Concluding remarks and related content

In this article I introduced barge transport systems and highlighted their characteristics and benefits to US and global economy. Simulation is a helpful tool that can improve the economic efficiency of barge transport systems. A simulation model can correctly reflect the overall river system and barge transport system, and can test and compare different barge routing and dispatching strategies, as well as compare overall system performance with varying barge and tugboat capacities.

Here are some related SCDA articles that might be of interest to you if you are interested in learning more about the benefits of industrial logistics simulations:

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As the core developer of salabim, I would like to show how this model could be implemented in salabim. I am sure the model will be easier and certainly have better animation.
Is there a GitHub of the SimPy model somewhere?

If you want to promote your product or tool, then you should make a blog about that. But please note this article is not about SimPy vs salabim, it is about barge transport. I have published a salabim example of yours on this blog in the past, and you are welcome to send more contributions comparing SimPy and salabim. But please here, under this article, your comment is not constructive. Thanks

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