DT-AI-3E, Digital Twins and AI for forestry operations planning with considerations of 3E

Purpose and goal

The project is a feasibility study that aims to develop a decision support prototype for planning forest logistics with support of AI and a digital twin. Weather conditions have a major impact on transport planning and are therefore integrated into the model. The goal is to increase delivery precision, reduce energy consumption and strengthen sustainability by balancing efficiency, energy and ecological impact (3E-objective). The prototype will form a basis for future development.

Expected effects and result

The project will investigate whether AI and digital twins can improve operational planning of harvesting and transport in forestry by integrating real-time data on road stock and road accessibility. The goal is to develop a decision support prototype in three steps: a digital twin for real-time data collection, AI for dynamic adjustment of delivery plans, and a web-based interface for visualizing the decision support. The aim is to improve delivery performance and reduce energy consumption.

Planned approach and implementation

The project consists of three work packages. WP1 utilizes a previously developed simulation-based multi-objective optimization model as a foundation for long-term operational planning, incorporating new functionality to optimize transport distances and manage current landing inventory and weather-affected road conditions. WP2 develops AI for dynamic transport planning using data from WP1. WP3 delivers a decision support prototype based on WP1 and WP2.