Aquaplot Technology Helps Businesses to Optimize
Get dynamic ETAs for all vessels en-route to your port/logistics hub.
Use this data for integrated planning of, for example, berthing schedules, vessel & pilot matching and coordination with hinterland activities.
Run simulations on routes in your network using decades of historical weather data.
Use this data to get a better picture of time and cost per route. More data-driven approach than relying for example on fixed percentage buffer.
Quickly estimate voyage cost to deliver your goods to different markets.
Combine this data with market prices and optimize your top and bottom line by re-routing vessels to the markets with the best cost/revenue ratio.
We want to help businesses run more efficiently with automation, holistic optimization and data-driven decision making.
By virtually planning a route and simulating vessel movements, better answers can be found for some of the most challenging questions, such as:
We develop nature-inspired artificial intelligence to help you find your answers.
Every functionality and service that we provide can be integrated into your products and services, internal tools and even your ERP.
Aquaplot is built on top of a proprietary routing technology that is inspired by how humans work together in teams. We call it EvoSwarm.
It takes proven concepts of nature-inspired artificial intelligence to the next level.
EvoSwarm is based on a fusion of two proven nature-inspired optimization techniques, evolutionary algorithms and particle swarm optimization. Extensive research and development went into further improving the approach by extending individual agents capabilities to for example memorize decisions and to plan ahead as well as introducing several information exchange mechanisms that allow generational knowledge transfer and coordination. It is further designed to allow deep domain knowledge to be used to increase performance. This is done by, for example, defining specific sub-swarms (teams) that follow particular strategies. For example, one strategy for finding a short route in a polygonal environment is to have a swarm dynamically build a subset of the visibility graph by performing a spatial branch-and-bound search.
Interested in learning more? Get in touch!