CTABlog
Planning, route optimization, constraints, execution criteria and practical lessons from complex logistics projects.
Explore articles grouped by logistics and route optimization keywords.
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
Rate management makes it possible to calculate the real cost of transport alternatives according to distance, weight, volume, zones, vehicles, carriers, surcharges and service conditions. Integrating these costs into planning allows routes to be optimised not only by mileage, but also by profitability and operational efficiency.
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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
Loading and unloading constraints directly affect whether a route is feasible: weight, volume, compatibility, schedules, service times, access and loading order can completely change the plan. Integrating them from the outset makes it possible to generate more realistic routes, reduce incidents and improve fleet utilisation.
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Technical blog
Cost per delivery measures how much it really costs to fulfil each order in a distribution operation. To calculate it properly, fixed costs, variable costs, mileage, hours, service times, failed deliveries and delivery density should all be considered, rather than simply dividing total cost by the number of deliveries.
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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
A TMS is a transport management system that helps centralise shipments, routes, costs, carriers, tracking and incidents. Its value increases when it includes route optimisation, allowing companies not only to manage operations but also to plan vehicles, orders, schedules and constraints more effectively.
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Technical blog
Route planning with time windows makes it possible to organise deliveries, collections or visits within each customer's available hours. Effective optimisation must balance distance, waiting times, vehicle capacity and operational constraints to build feasible and efficient routes.
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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
Route optimisation software helps organise orders, vehicles, schedules and constraints to generate more efficient logistics plans. It does more than calculate the shortest route: it can reduce mileage, improve fleet utilisation, meet delivery time windows and support better operational decisions.
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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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Combine technical articles with case studies to understand how LOGISPLAN models constraints, resources and objectives in real operations.