The AI-based simulations revolutionize complex logistics problems in complex systems.

    The AI-based simulations revolutionize complex logistics problems in complex systems.  

Complex logistics problems cause loss of fuel and time in logistics companies. The logistics problems are always complicated because logistics means merchandise transportation from the factory to the warehouse, and then to the customer's door, which can be on the fourth floor in some old office building. Things like suddenly beginning road works, building sites, and elevator problems are things. That causes problems in everyday logistics. 

One error or difference between plans and reality can cause a situation in which nothing works. People can make mistakes. Sometimes in the warehouse happens. That somebody forgets to mark where other people can find merchandise. A person can start suddenly to do something else, and simply forget to read the QR code from the package. The AI that sees the QR-codes can help to find that kind of package. The surveillance camera or small quadcopter can read all QR codes or RFID recognition chips. The quadcopter can also control logistics in warehouses, and that system is easy to install. 

When the lorry starts from the final warehouse to the customer, the problem can be in the house where the driver should deliver the cargo. Doors can be too small there can be some other work going on or an elevator that should used to carry cargo to floors can be stuck. So the problem somewhere in the route can cause very bad problems in delivery chains. 

"Researchers from MIT and ETH Zurich have developed a machine learning-based technique to speed up the optimization process used by companies like FedEx for routing packages. This approach, which simplifies a key step in mixed-integer linear programming (MILP) solvers and tailors the process using a company’s own data, has resulted in a 30 to 70 percent increase in speed without sacrificing accuracy. It has potential applications in various industries facing complex resource-allocation problems". (ScitechDaily.com/AI Revolutionizes Complex Problem-Solving in Logistics and Beyond)

The interactive AI that collects information from different sources can be a useful tool for route planners. If the information about stuck elevators will go to logistics automatically and suppliers know that the elevator is out of use at a certain time, that saves money. 

In the interactive, AI-based simulation the system can use aerial photographs, knowledge of the load capacities on yards, and doors, and the elevator's dimensions making it possible for the AI to simulate how the merchandise fits in some place. The complete simulation requires complete knowledge of the system. The AI can move virtual lorries and virtual merchandise to find out the most effective way to use time and fuel. 

But then we must realize what logistics is. Its routing and transportation merchandise. The same programs that are used for solving transportation and logistics problems in multiple environments can used to model how information travels in complex data systems. In Internet hard disks routers and swithches replacing lorries and warehouses. 

The same systems that solve complex logistics problems in the so-called real world can solve routing problems on the internet where the worst problems are the data traffic rush in some routers. There is a possibility that an overloaded router routes the data package in the wrong direction. In that case, the system must find a new route for those data packages. The AI-based control system that controls the internet in its entirety can solve the problem of some routers and server overload. 

As we know the same systems that engineers use to control and simulate things on the Internet, can be used to simulate things. On how to route information in microchips and human nervous systems. These kinds of systems are the next-generation tools for engineering and medical systems. 

https://scitechdaily.com/ai-revolutionizes-complex-problem-solving-in-logistics-and-beyond/