CTASEO topic
Genetic algorithms are a combinatorial optimisation technique widely used in logistics planning as the number of vehicles, stops and constraints grows. LOGISPLAN technical content on how they work and their role in route optimisation at scale.
Technical content tagged with this keyword cluster for route planning and logistics optimization.
Technical blog
In compartmentalised logistics planning, vehicles have tanks, chambers, zones or internal divisions, so analysing total capacity alone is not enough. Good optimisation must decide which orders and products go into each compartment, respect compatibility rules and build feasible, efficient routes adapted to the actual load.
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Technical blog
AI applied to transport makes it possible to move from static planning to predictive and adaptive planning. Machine learning helps organisations learn from real data, improve estimates and anticipate risks, while genetic algorithms use that information to generate routes that are more robust, efficient and able to adapt to changes during operations.
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Technical blog
Artificial intelligence in transport combines techniques such as genetic algorithms and machine learning to improve logistics planning. While machine learning learns from historical data and enables better estimates of times, risks and incidents, genetic algorithms use that information to generate efficient, feasible routes adapted to the real constraints of each operation.
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Technical blog
Genetic algorithms are particularly well suited to route optimisation because they can tackle real-world logistics problems involving multiple constraints, objectives and changing conditions. Unlike more rigid methods, they work with populations of solutions, combine promising alternatives and evolve towards efficient, feasible plans tailored to each operation.
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Technical blog
In a genetic algorithm, it is not enough to generate many possible solutions. It is also necessary to know how to compare one solution with another. This is done using an evaluation function, also known as a fitness function. This function measures the quality of each individual in the population and determines which solutions are more likely to survive, reproduce and pass their characteristics on to subsequent generations.
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Technical blog
In a genetic algorithm, every individual in the population represents a candidate solution to the problem. In simple problems, this can be relatively straightforward. In real distribution, transport and logistics-planning problems, however, representing a solution can become highly complex.
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Technical blog
Genetic algorithms are a branch of artificial intelligence used to solve complex optimisation problems. Genetic algorithms emerged in the 1970s through the work of John Henry Holland and David Goldberg. Since then, their use in solving search and optimisation problems has been extensively validated. LOGISPLAN is developed using optimisation techniques based on genetic algorithms.
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