Function and advantages of the ant algorithm
The ant algorithm, often referred to in the literature as Ant Colony Optimization (ACO), is an algorithm for the approximate solution of complex optimization problems. This method of combinatorial optimization, also known as metaheuristics, is based on the behaviour of ants in their search for food.
As the name suggests, the model is inspired by nature. The ant simply runs off in search of food. It marks its path with volatile scents, firstly so that it can find its way back and secondly so that other ants can follow its path to the food if it is successful. If another ant now finds a shorter route, the scents on its route are correspondingly more intense, as the release was less long ago. Consequently, subsequent ants follow this path, which further intensifies the scent trail.
If the original fastest route is suddenly blocked, the fastest route can be determined again based on the returning ants and the intensity of their scent trails. In this way, the best route can be found within a short space of time and communicated in a way that everyone can understand – without any higher-level coordination.
From nature to industry
Back in the 1990s, researchers were already working on transferring this know-how from nature to industry. Today, the ant algorithm is used in many scenarios, such as route optimization in production and intralogistics, the switching of communication channels such as telephone and internet or transport management in logistics.
What all these areas of application have in common is that the highly complex route planning is not elaborately calculated and centrally coordinated, but that each object involved is capable of making decisions and taking action independently and decides on the optimum solution based on the algorithm and the current data of the other objects.
Ant algorithm in intralogistics and production
The internal material flow is a complex network with countless influencing factors such as incoming orders, route occupancy, cut-off time, goods availability or machine and personnel availability. The more variables and parameters flow into a control process, the more complex its mathematical calculation becomes.
Various mathematical methods allow these calculations, whereby a distinction can be made between exact calculations and heuristics (analytical approach based on scenarios that are likely to occur). While exact calculations provide a 100% reliable result, so-called metaheuristics, such as the ant algorithm, only provide a probable best result. However, the advantage of the heuristic method is the significantly lower calculation effort, which requires only a fraction of the effort of an exact calculation.
“In the planning phase of a logistics process, where time is not of the essence, you can and should work with as many exact methods as possible,” explains mathematician Thomas Runkler in an interview with brand eins. However, if unforeseen situations arise during operation, it is important to react as quickly as possible. “And that’s where the ants are closer to real life.”
These combinatorial optimization processes enable a dynamic material flow that adapts independently to the current situation. Particularly in the context of Industry 4.0 developments, the trend in some areas is therefore towards decentralized control intelligence, which also provides a basis for the smart factory and various cyber-physical systems.
Information on the simulation of intralogistics systems can be found under material flow simulation.
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