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Machine learning + simulation modeling
27-Nov-2019
Machine Learning is one of the directions of Industry 4.0 concept related to Artificial Intelligence being enhanced by Big Data.
Simulation modelling is a technology that allows to duplicate production and business processes in the digital environment. Such digital twins possess all the necessary qualities and properties of their originals.
What happens when technologies merge?
In context of simulation modeling, machine learning is a tool that makes dynamic data optimization possible according to environment changes. It allows to use the actual optimal parameters but not the average ones.
AnyLogic and machine learning combination provides new opportunities, such as:
· building dependencies basing on historical data
· calculating importance of all factors considering different difficulty levels
· analyzing incoming parameters and visualizing the outgoing information flow online
In context of simulation modeling, machine learning is a tool that makes dynamic data optimization possible according to environment changes. It allows to use the actual optimal parameters but not the average ones.
AnyLogic and machine learning combination provides new opportunities, such as:
· building dependencies basing on historical data
· calculating importance of all factors considering different difficulty levels
· analyzing incoming parameters and visualizing the outgoing information flow online
Workshop model with elements of machine learning
ABOUT MODEL: end products storage workshop with two cranes, two conveyor belts through which the products are delivered, a loading area for dump trucks and storage area.
PAINS to solve:
· unpredictable loading and unloading locks;
· one crane being block by another;
· the need for regular and emergency repairs.
BUSINESS GOALS
Increase the efficiency of the end products storage workshop.
MAIN TASKS:
· minimize downtime;
· increase total output of cranes;
· increase workshop workload;
· reduce crane blocking probability.
ABOUT MODEL: end products storage workshop with two cranes, two conveyor belts through which the products are delivered, a loading area for dump trucks and storage area.
PAINS to solve:
· unpredictable loading and unloading locks;
· one crane being block by another;
· the need for regular and emergency repairs.
BUSINESS GOALS
Increase the efficiency of the end products storage workshop.
MAIN TASKS:
· minimize downtime;
· increase total output of cranes;
· increase workshop workload;
· reduce crane blocking probability.
The solution is achieved by real time priority distribution between cranes with usage of machine learning.
Initially the learning algorithm receives historical data and builds dependencies. During the model execution the dependencies are being adjusted according to the current position of cargo in the cranes operation area.
As a result, the tasks are being optimally split between the cranes. This leads to downtime minimization and overall utilization and entire enterprise output increase.
Initially the learning algorithm receives historical data and builds dependencies. During the model execution the dependencies are being adjusted according to the current position of cargo in the cranes operation area.
As a result, the tasks are being optimally split between the cranes. This leads to downtime minimization and overall utilization and entire enterprise output increase.
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