The AI Course on Transport Route Optimisation provides specialised training in application of artificial intelligence to improve logistics efficiency. Through these teaching units, pupils will learn from the Fundamentals of route planning and transport up to the implementation of smart models, including evolutionary algorithms, swarm techniques and probabilistic optimisation, to design solutions that optimise costs, timeframes and resources in real-world scenarios.
Cross-cutting
AI Academy
10 a.m.
AI for transport route optimisation
AI Academy
10 a.m.
Introduction
Objectives
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Understanding the logistics principles and factors affecting route efficiency.
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To analyse how the Artificial intelligence solves complex route-planning problems.
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Understand and apply heuristics, meta-heuristics, evolutionary algorithms and swarm techniques in logistics.
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Prepare and treat geospatial data (GIS) y transport data for optimisation models.
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Integrate Map APIs, positioning systems and logistics management software.
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To design solutions that enable the real-time monitoring and dynamic route adaptation.
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Measuring the effectiveness of optimisation through metrics and performance analysis.
Table of Contents
TEACHING UNIT 1. DISCOVER THE LOGISTICAL FUNDAMENTALS OF ROUTE PLANNING
Understanding transport systems from a logical perspective
Classification of distribution networks and types of transmission
Factors affecting route efficiency: costs, times and capacity
Analysis of common constraints in real-world scenarios
TEACHING UNIT 2. EXPLORE HOW AN AI THINKS WHEN SOLVING LOGISTICS ROUTING PROBLEMS
Smart methods for solving logistics routing problems
A comparison of heuristics, meta-heuristics and machine learning approaches
Evolutionary algorithms and their application to route planning
Swarm techniques: ant colonies and particle optimisation
Probabilistic approaches and stochastic search applied to routing
TEACHING UNIT 3. DESIGN INTELLIGENT MODELS THAT UNDERSTAND AND OPTIMISE REAL-WORLD ROUTES
Data preparation for optimisation systems
Representation of routes and constraints in computational environments
Acquisition and processing of geospatial data (GIS)
Integration of map APIs and real-time positioning systems
The application of AI models to route planning
TEACHING UNIT 4. IMPLEMENTATION, MONITORING AND ADAPTATION OF OPTIMISED ROUTES
Preparing the deployment environment for smart solutions
Integration with logistics management software (ERP, WMS, TMS)
Real-time monitoring systems for fleets and deliveries
Automatic response to unforeseen events: incidents, delays and traffic
Dynamic re-routing: continuous route adjustment
Metrics for measuring the effectiveness of optimisation
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