RAISE (Robotics & AI for Socio-economic Empowerment) is an innovation ecosystem funded by the Italian Ministry of University and Research (MUR) under the PNRR. The goal is to develop and implement solutions based on robotics and artificial intelligence (AI) to improve efficiency and sustainability across several strategic sectors.

Within RAISE, Spoke 4 focuses on smart and sustainable ports, leveraging advanced solutions to optimize logistics operations and reduce environmental impact. To address these challenges, our team is working on two core modules.

Our Solutions for the Quay Crane Planning Problem

One of the most critical aspects of port management is the planning of loading and unloading operations for container ships, a problem known as the Quay Crane Planning Problem (QCPP). The QCPP concerns the allocation and sequencing of quay cranes, with the aim of reducing handling times, optimizing energy consumption, and improving operational safety. At the same time, planning must take into account a detailed representation of vessel bays and operational flows, making an advanced simulation platform essential.

To address this need, our team is developing two integrated modules: one dedicated to visualization and simulation of operations, and the other focused on optimizing port activities.

Module 1A – 3D Simulation for Port Planning

Module 1A, developed by our team, introduces an advanced system for dynamic visualization of container ship loading and unloading plans. The platform, called 3D Master Bay Visualizer (3D-MBPV), allows decision-makers to plan and simulate operations in real time, enabling advanced interaction with data.

Key features include:

• Interactive 2D and 3D visualization for detailed analysis of loading plans
• Container-based modular architecture orchestrated via Docker Swarm
• Intuitive user interface developed with Angular for smooth navigation and access to simulation tools
• Integration with PostgreSQL database, ensuring high scalability and reliability
• Traffic management via Traefik and advanced security through Keycloak for user authentication

Thanks to this platform, decision-makers can explore different operational scenarios, optimizing resource allocation and minimizing the risk of inefficiencies.

Module 1B – Optimization of Loading and Unloading Operations

Module 1B, currently under development, focuses on optimizing the Quay Crane Planning Problem (QCPP), a complex problem involving the management of quay cranes for container loading and unloading operations. The proposed approach combines AI techniques and combinatorial optimization to improve operational performance.

Main objectives:

• Minimization of operation completion time, ensuring higher logistics efficiency
• Optimization of energy consumption, reducing environmental impact
• Reduction of the number of cranes used, improving resource utilization

Optimization Technologies and Strategies

To address the QCPP, a metaheuristic approach inspired by natural evolution and adaptation processes is adopted. In particular, genetic algorithms play a key role in searching for optimal solutions, replicating natural selection to progressively improve operational configurations.

These algorithms enable exploration of large solution spaces and identification of efficient configurations through mutation, crossover, and selection processes. Combined with techniques such as local search and greedy randomized approaches, solutions can be further refined, ensuring more efficient cargo handling operations.

Impact and Benefits

The integration of the 3D Master Bay Visualizer (3D-MBPV) with QCPP optimization models provides an advanced solution for port operations management. This combination enables more accurate operational planning, reducing vessel waiting times and improving logistics flow.

Furthermore, AI-based optimization significantly reduces energy consumption, contributing to port sustainability. The adoption of advanced technologies not only increases port competitiveness but also promotes a more ecological and resilient approach to maritime sector challenges.