AGROAMB

Design and develop an Artificial Intelligence–based system for AGROAMB PRODALT SL, focused on optimizing waste collection routes for both in-house fleets and external service providers.

The solution incorporates a GIS (Geographic Information System) platform to visualize routes, container locations and other relevant geospatial variables. It also integrates data from multiple sources—such as container sensors, online service requests and historical records from the company’s ERP system—and applies advanced Artificial Intelligence, Machine Learning and mathematical optimization techniques to compute efficient routes.

The system considers key operational factors such as vehicle capacity, availability, time windows and service urgency, with the goal of improving operational efficiency, reducing costs and enhancing service quality.

Specific Objectives

• AI prototype development: Implementation of an intelligent system for waste collection route optimization using Machine Learning algorithms and optimization techniques.
• Data integration: Design of different mechanisms for collecting and unifying information from sensors, digital platforms and internal company systems.
• Advanced route optimization: Application of linear programming, metaheuristic algorithms (e.g., genetic algorithms) and predictive models to calculate optimal routes.
• Efficiency and customer service improvement: Reduction of operational costs and implementation of more agile and effective communication channels with customers through the platform.