CTASEO topic
Artificial intelligence is transforming logistics planning, from demand forecasting to route optimisation that accounts for learning and operational constraints. LOGISPLAN Blog articles on AI in transport, predictive models and automated decisions in real operations.
Technical content tagged with this keyword cluster for route planning and logistics optimization.
Technical blog
Reducing logistics costs requires more than simply cutting resources. Intelligent planning makes it possible to optimise routes, reduce mileage, improve fleet utilisation, avoid waiting times and incidents, control cost per delivery and make data-driven decisions without compromising service levels.
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Technical blog
Last-mile optimisation helps reduce costs, improve on-time performance and make better use of the fleet in operations with many deliveries, constraints and daily changes. Good planning must take account of mileage, time windows, capacities, service times, incidents and real operational data.
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Technical blog
Reducing distribution mileage is not simply a matter of making routes shorter. Good planning must group orders, avoid overlaps, make better use of the fleet and respect schedules, capacities and operational constraints in order to lower costs without compromising service.
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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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