Titulo Estágio
A Transfer Learning-based Approach for solving the Haulier Capacity Matching Problem of 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 Porto de 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 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.
In today's rapidly evolving world, the importance of Artificial Intelligence (AI) has become increasingly prominent. AI technologies have revolutionized various industries and sectors, driving innovation, efficiency, and improved decision-making processes.
The problem of haulier capacity matching involves efficiently matching available hauliers (trucks or carriers) with transportation demands or shipments. This process requires considering factors such as haulier availability, shipment characteristics, geographical constraints, delivery deadlines, and specific requirements.
The Dial-a-Ride Problem (DARP) is a transportation problem that involves scheduling and routing vehicles to provide transportation services for passengers with specific pickup and delivery requests. The problem typically arises in scenarios where passengers need to be transported between various locations, such as medical appointments, social events, or other personal activities. An instantiation of this problem is that of Uber Technologies Inc.
Although there are similarities between the two problems, the specific characteristics and constraints differentiate haulier capacity matching from the Dial-a-Ride Problem. However, both problems require efficient resource allocation, considering constraints and optimization objectives. Techniques and methodologies developed for one problem domain can often provide insights and inspiration for addressing the other, but the solutions need to be tailored to the specific requirements and constraints of each problem.
Transfer learning is a machine learning technique that enables a model to leverage knowledge learned from one task and apply it to another related or different task. It involves transferring the knowledge gained from a source domain, where labeled data or pre-trained models are abundant, to a target domain, where the amount of labeled data may be limited or the task may differ.
In transfer learning, the pre-trained model or the knowledge gained from the source domain is used as a starting point or a foundation for training a model on the target domain. This pre-training provides the model with a set of learned features, representations, or parameters that capture general patterns and knowledge from the source data.
Given that the Haulier capacity matching and the Dial-a-Ride Problem (DARP) share similarities in terms of resource allocation and constraints, transferring learning from the DARP problem to haulier capacity matching can be justified for various reasons.
By leveraging transfer learning from the Dial-a-Ride Problem, an AI system for haulier capacity matching can benefit from the insights, algorithms, and expertise developed in the transportation optimization domain. The knowledge transferred from DARP can provide a strong foundation for resource allocation, constraints handling, and optimization strategies in haulier capacity matching, ultimately improving the efficiency and effectiveness of the matching process.
Objetivo
The aim of this project is to develop an AI system for the haulier capacity matching problem of the Port of Sines by making use of the machine learning technique of transfer learning . The research objectives to accomplish this aim are:
1. Data Collection and Preprocessing: Collect relevant data from both the DARP domain and the haulier capacity matching domain. This includes historical transportation data, haulier profiles, demand information, and any other relevant data sources. Preprocess the data to ensure compatibility and uniformity between the datasets.
2. Pretraining on DARP: Train a model, such as a neural network or a reinforcement learning agent, on the DARP dataset using appropriate algorithms and techniques. This initial training on DARP serves as the source domain for transfer learning.
3. Feature Extraction: Extract meaningful features or representations from the pre-trained model that capture relevant information about resource allocation, route optimization, capacity constraints, and other important aspects of transportation logistics.
4. Model Adaptation: Design or modify the model architecture to accommodate the haulier capacity matching problem. This may involve adding or modifying specific layers, adapting input-output dimensions, or incorporating additional constraints and requirements specific to the haulier domain.
5. Fine-tuning on Haulier Capacity Matching: Use the extracted features and the adapted model architecture as the starting point for training on the haulier capacity matching dataset. Fine-tune the model using the labeled data from the haulier domain, adjusting the parameters to optimize the allocation of hauliers to transportation demands.
6. Evaluation and Iterative Refinement: Evaluate the performance of the transfer learning model on haulier capacity matching tasks. Assess metrics such as resource utilization, matching accuracy, customer satisfaction, and overall system efficiency. Iterate on the model and training process, refining the architecture, hyperparameters, or data representation as needed to improve performance.
7. Deployment and Continuous Learning: Deploy the transfer learning model into a production environment for real-world haulier capacity matching. Continuously collect feedback, monitor the system's performance, and gather new data to further improve the model through iterative updates and refinements.
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 AI system [Nov – Jan]
4- Thesis proposal writing [Dec – Jan]
Plano de Trabalhos - Semestre 2
5- Improvement of the AI 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/).*
A 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 📩