Titulo Estágio
A Recommender System-based Approach for the Nexus Haulier Capacity Matching Problem in Porto de Sines
Áreas de especialidade
Sistemas Inteligentes
Sistemas Inteligentes
Local do Estágio
Centre for Informatics and Systems of the University of Coimbra (CISUC), at the Department of Informatics Engineering of the University of Coimbra
Enquadramento
The NEXUS Agenda consortium, led by the Port of Sines, comprises 35 partners who share a common goal of developing innovative solutions to achieve a Green and Digital Transition Agenda. The consortium represents the entire value chain, including port authorities, maritime and terminal operators, railway operators, carriers, dry ports, logistics operators, technology suppliers, importers, exporters, as well as universities and research institutes. The expertise and skills of these partners play a crucial role in realizing this pioneering Agenda.
Within the NEXUS framework, a key objective is to harness the vast pool of high-quality data available for the development of Big Data Analytics and Artificial Intelligence Solutions. This internship opportunity is part of Work Package 2 (WP2), which focuses on creating specialized multimodal network and logistics applications to tackle this challenge. All resulting products and services will be designed and demonstrated in collaboration with end users, including terminal operators, road and rail operators, dry ports, and authorities within the multimodal logistics networks.
One specific task within WP2 aims to optimize the utilization of haulier resources, leading to cost savings and faster shipments. This task involves leveraging data collected from other sources to gain insights into the current state of logistic networks. By applying artificial intelligence (AI) methods, we aim to forecast the supply and demand of haulier services across the entire logistics network. These forecasts, combined with optimization algorithms, will maximize resource utilization to their highest possible capacity. This endeavor encompasses various challenges such as developing accurate forecasting mechanisms, efficient data collection, problem characterization, modeling, and solving the optimization problem. The proposed activities stem from these challenges and seek to address them effectively.
There are several recommender system approaches and algorithms that can be considered for generating recommendations in the context of capacity matching such as:
-Collaborative Filtering: Collaborative filtering techniques recommend items or resources based on the similarity between users or their past behavior. This approach identifies users with similar preferences and suggests resources that those similar users have liked or used.
- Content-Based Filtering: Content-based filtering recommends resources based on their attributes or characteristics. It analyzes the features or content of the resources and matches them with the user's preferences. For example, if a user has shown a preference for certain attributes of resources, such as genre or topic, the system recommends other resources with similar attributes.
-Hybrid Approaches: Hybrid recommender systems combine multiple techniques to provide more accurate and diverse recommendations. These systems leverage the strengths of different approaches to overcome their individual limitations. For example, a hybrid approach can combine collaborative filtering and content-based filtering to benefit from both user behavior patterns and resource characteristics.
-Context-Aware Recommender Systems: Context-aware recommender systems consider contextual information such as time, location, or user context to generate personalized recommendations. In the capacity matching scenario, the context-aware recommender system can consider factors such as resource availability, user location, time constraints, and user preferences to recommend the most suitable resources.
The recent growth in the volume of data available for recommender systems has led AI scientists to adopt deep learning models as a means to enhance recommendation quality. This shift is driven by the need to leverage highly expressive models that can effectively learn from complex patterns within the data. Deep learning recommender models build upon existing techniques such as factorization and embeddings, which handle categorical variables by representing them as learned vectors. These models leverage various deep neural network architectures, such as feedforward neural networks, convolutional neural networks, and recurrent neural networks, to extract features and build expressive models. Large Language Models (LLMs), such as OpenAI's GPT-3, have demonstrated remarkable capabilities in natural language processing and understanding, enabling more sophisticated approaches to recommendation tasks. All these recent advances in deep learning have paved the way for more accurate and personalized recommendations across a wide range of domains beyond recommender systems, including image, text, and speech analysis.
Objetivo
The aim of this project is to build a recommender system for the haulier capacity matching problem of the Porto de Sines and provide optimal matching performance. The research objectives to accomplish this aim are:
1. Formalising the problem of haulier capacity matching as a problem of recommendation of shipments (items/resources) to trucks (users), which may involve:
- User profiling: The recommender system can gather information about the users' preferences, requirements, or historical usage patterns. This information can be used to create user profiles that capture their specific needs and constraints.
- Resource profiling: Similarly, the recommender system can collect information about the available resources, their capacities, capabilities, and other relevant attributes. This profiling helps in understanding the characteristics of each resource.
- Recommendation generation: The recommender system algorithm incorporates a matching algorithm that considers user and resource profiles to generate recommendations. It takes into account factors like user preferences, resource availability, compatibility, and constraints. By leveraging this algorithm, the system suggests suitable resources to users, optimizing capacity utilization. The algorithm considers various factors and can be customized to handle capacity matching requirements, eliminating the need for a separate matching algorithm. It aligns recommendations with capacity matching objectives by considering resource availability, capacity constraints, and other relevant factors. Proper configuration and fine-tuning of the recommender system algorithm enable it to effectively match users with appropriate resources, facilitating capacity matching.
- Feedback and adaptation: As the system receives feedback from users regarding the recommended resources or services, it can adapt and refine the recommendations over time. This feedback loop helps improve the accuracy and relevance of the recommendations.
2. Assessment of the matching performance of different recommender systems approaches (e.g., collaborative-based, content-based, knowledge-based) relying on recent methods (deep learning, including LLMs) that might be considered for the recommender system;
Plano de Trabalhos - Semestre 1
1- State of the art [Sept – Oct]
2- Problem statement, research aims and objectives [Nov]
3- Design and first implementation of the recommender system [Nov – Jan]
4- Thesis proposal writing [Dec – Jan]
Plano de Trabalhos - Semestre 2
5- Improvement of the recommender system [Feb – Apr]
6- Experimental Tests [Apr – May]
7- Paper writing [May – Jun]
8- Thesis writing [Jan – Jul]
Condições
The work should take place at the Centre for Informatics and Systems of the University of Coimbra (CISUC) at the Department of Informatics Engineering of the University of Coimbra in the scope of the Nexus research project (https://nexuslab.pt/).*
An 930,98 euros per month scholarship is foreseen for 6 months. The attribution of the scholarship is subject to a public application.
*Project "Agenda Mobilizadora Sines Nexus". ref. No. 7113, supported by the Recovery and Resilience Plan (PRR) and by the European Funds Next Generation EU, following Notice No. 02/C05-i01/2022, Component 5 - Capitalization and Business Innovation - Mobilizing Agendas for Business Innovation.
The candidate must have a very good background knowledge in Artificial Intelligence, especially in the areas of Artificial Intelligence that the internship falls within.
Observações
Advisors:
Luís Macedo, Filipe Araújo.
Orientador
Luís Macedo
macedo@dei.uc.pt 📩