1 Topics Related to Data Analytics, Data Science and Machine Learning
The topics in this category involve a comprehensive data analytics or machine learning pipeline to analyze an existing dataset. This process may include the following steps:
- Exploratory Data Analysis
- Data Preprocessing
- Model Building
- Model Evaluation
These topics are ideal for students who are familiar with Python or R and have a strong interest in data science. Advanced programming skills are not required.
1.1 Applying Causal AI Algorithm for Enhanced Component Predictive Diagnostics
Background/Motivation/Problem
Traditional predictive diagnostics methods often rely on correlations without addressing the underlying causality of component failures. Causal AI algorithms offer the capability to identify root causes and hidden relationships, providing more insightful predictive diagnostics. The research gap involves exploring causal AI techniques to improve predictive maintenance in industrial components, moving beyond mere correlation-based models.
Research Questions
- What role do causal inference and discovery algorithms play in predicting component faults?
- How can causal impact models evaluate the effectiveness of maintenance actions or software updates?
- What are the key factors contributing to frequent component failures, and how can they be identified using causal inference algorithms?
Tasks
- Study the application of causal inference and discovery algorithms in predictive diagnostics.
- Propose causal inference algorithms to identify factors contributing to component failures.
- Develop causal discovery techniques to analyze component sensor data and uncover hidden relationships.
- Suggest causal impact models to evaluate how changes in predictive diagnostic systems improve component reliability.
Ideal for: Students interested in machine learning, causal modeling, and predictive diagnostics, especially those working with algorithms and vehicle data analysis.
Contact: egraha@constructor.university
1.2 Leveraging Large Language Models (LLM) for Predicting Vehicle Component Wear and Tear
Background, Motivation, Problems, and Research Gaps
Large Language Models (LLMs) like GPT and BERT can process vast amounts of unstructured data, such as maintenance logs and sensor reports, to identify patterns in vehicle component degradation. Despite this potential, there is a gap in applying LLMs specifically to predict component wear based on historical data and to compare their performance with traditional models. Addressing this gap could lead to more effective predictive maintenance strategies.
Research Questions
- How can LLMs be adapted to predict vehicle component wear and tear using historical data?
- How do LLM-based predictive models compare to traditional machine learning techniques in terms of accuracy?
- Can LLMs provide actionable, real-time insights for vehicle maintenance and diagnostics?
Tasks
- Investigate the application of LLMs for predicting vehicle component wear using diverse data sources, including maintenance logs and sensor data.
- Train an LLM to process unstructured data and identify patterns in component degradation.
- Develop a predictive model using LLMs to estimate the remaining lifespan of vehicle components.
- Compare the LLM-based model’s performance with traditional machine learning techniques for predicting component failures.
Ideal for: Students interested in LLM, predictive modeling, and automotive systems, with experience in Python.
Contact: egraha@constructor.university
1.3 Using the LSTM Algorithm for Predicting Industrial Component Faults
Background/Motivation/Problem
Efficient maintenance and reduction of downtime in industrial systems heavily rely on accurate prediction of component failures. Traditional methods struggle with real-time predictions due to complex wear patterns. LSTM (Long Short-Term Memory) algorithms offer a promising solution by modeling time-series data to identify potential faults. However, the research gap exists in developing robust predictive models using LSTM specifically tailored for industrial component breakdowns.
Research Questions
- How can LSTM algorithms be effectively applied to predict industrial component breakdowns?
- What database structure best supports the storage and analysis of failure patterns for predictive maintenance?
- How accurate and reliable is the LSTM-based predictive model when tested with real-world data?
Tasks
- Conduct a literature analysis on LSTM applications in predicting industrial component faults.
- Develop a database concept to store component failures and wear patterns.
- Implement an LSTM-based predictive algorithm tailored to a case study and estimate the time before component failures.
- Test the developed algorithm with real-world data to assess its accuracy.
Ideal for: Students interested in programming and machine learning, with a focus on AI and industrial or vehicle technology.
Contact: egraha@constructor.university
1.4 Comparative Study of Causal Discovery Methods: Machine Learning Approaches (Causal Trees/Forests) vs. Functional Models (LiNGAM, RESIT)
Background, Motivation, Problems, and Research Gaps:
Understanding causal relationships is crucial for informed decision-making in fields like healthcare, economics, and social sciences. Causal discovery methods are divided mainly into two categories: machine learning-based methods (e.g., Causal Trees and Causal Forests) and functional models (e.g., Linear Non-Gaussian Acyclic Model (LiNGAM), Regression with Subsequent Independence Test (RESIT)).
Machine learning models like Causal Trees and Forests are popular for their ability to handle complex, non-linear interactions and to estimate heterogeneous treatment effects. Functional models, on the other hand, rely on explicit mathematical formulations to identify causal directions, offering direct interpretability. Despite the individual strengths of these approaches, there is a lack of comprehensive comparative analysis between machine learning-based causal discovery and functional models, particularly in terms of accuracy, robustness, and interpretability. This research aims to fill this gap by evaluating and contrasting these methods in various data scenarios.
Research Questions:
- How do machine learning-based causal discovery methods (e.g., Causal Trees, Causal Forests) compare with functional models (e.g., LiNGAM, RESIT) in terms of accuracy in identifying causal relationships?
- What are the strengths and limitations of each approach in different types of data, including linear, non-linear, and noisy datasets?
- How do machine learning-based and functional causal discovery methods differ in terms of interpretability and computational efficiency?
Tasks:
- Conduct a literature review on causal discovery methods, focusing on machine learning-based approaches (Causal Trees, Causal Forests, NOTEARS) and functional models (LiNGAM, RESIT).
- Implement the selected causal discovery methods using Python libraries such as CausalML, EconML for causal trees/forests, and CausalDiscoveryToolbox (cdt), CausalLearn for functional models.
- Apply these methods to real-world datasets with known causal structures, incorporating varying levels of complexity and noise.
- Compare the performance of these methods based on metrics like Structural Hamming Distance (SHD), True Positive Rate (TPR) / Sensitivity / Recall, False Positive Rate (FPR), Edge Orientation Accuracy, etc.
- Analyze the results to identify scenarios where one approach outperforms the other and provide recommendations for selecting appropriate methods for different types of causal discovery tasks.
Ideal for:
Students with a background in data science and machine learning who are interested in causal inference, model comparison, and the practical application of different causal discovery techniques. Proficiency in Python and familiarity with libraries such as scikit-learn, Causal Learn, CausalDiscoveryToolbox (cdt), CausalML, and lingam will be advantageous.
1.5 Uncovering Causal Relationships in Text Documents with Large Language Models (LLMs)
Background, Motivation, Problems, and Research Gaps:
Causal discovery is crucial for understanding cause-effect relationships in various domains like healthcare, economics, and social sciences. Traditional causal discovery methods rely heavily on structured numerical data, but a vast amount of causal knowledge exists in unstructured text formats, such as research papers, news articles, and policy documents. With recent advancements in natural language processing (NLP), Large Language Models (LLMs) like GPT-3 and GPT-4 have demonstrated impressive abilities in understanding complex language patterns, including the identification of causal relationships embedded in text.
However, using LLMs for causal discovery from text remains underexplored. The challenge lies in extracting meaningful causal relationships while differentiating correlation from true causation. Existing research has yet to fully utilize LLMs’ potential in automatically extracting and interpreting causal information from large corpora of text data. This research aims to bridge this gap by exploring how LLMs can be leveraged for causal discovery in unstructured text documents.
Research Questions:
- How effectively can LLMs extract causal relationships from unstructured text documents?
- What are the limitations of using LLMs in distinguishing between correlation and true causation in textual data?
- How can prompt engineering or fine-tuning improve the accuracy of causal extraction using LLMs?
Tasks:
- Conduct a literature review on causal discovery methods, focusing on techniques using LLMs for text analysis.
- Collect and preprocess a dataset of text documents from a domain of interest (e.g., medical articles, policy papers) that contain causal statements.
- Use prompt engineering and/or fine-tuning to train an LLM (e.g., GPT-3, GPT-4) for extracting causal relationships from the text.
- Extract causal pairs and represent them in a structured format (e.g., causal graphs) using tools like networkx.
- Evaluate the effectiveness of the LLM-based causal discovery by comparing the extracted relationships against known causal knowledge or benchmarks.
- Analyze the results to identify strengths, limitations, and potential improvements in using LLMs for causal discovery from text.
Ideal for:
Students interested in natural language processing, causal inference, and the application of large language models to complex data analysis tasks. Basic knowledge of Python and experience with NLP tools (e.g., Hugging Face Transformers, OpenAI API) will be advantageous. This project is ideal for those curious about how advanced AI models can be applied to extract meaningful insights from unstructured textual data.
1.6 Causal AI in Federated Learning for ESG Data Causal Analysis
Background, Motivation, Problems, and Research Gaps
Understanding the causal relationships within ESG data (e.g., the impact of sustainability initiatives on company performance) is more informative than simple correlations. Causal AI models can identify and quantify these relationships, but using centralized data for such analysis raises privacy concerns. By integrating causal AI into federated learning, we can perform causal analysis on distributed ESG data without exposing sensitive information. This study addresses the research gap in combining causal inference techniques with federated learning for privacy-preserving causal analysis of ESG factors.
Research Questions
- How can causal AI be integrated into federated learning for ESG data analysis?
- What challenges arise when applying causal inference in a federated learning context?
- How can federated causal AI models improve the understanding of ESG factors’ impact on sustainability performance?
Tasks
- Implement a federated learning system using PySyft or TensorFlow Federated.
- Integrate causal AI models (e.g., CausalNex, DoWhy) into the federated learning environment.
- Develop a mechanism to aggregate causal analysis results from different clients while preserving privacy.
- Test the integrated system on ESG datasets to identify and interpret causal relationships.
Ideal for: Students with a strong interest in Python programming, causal inference, federated learning, and ESG data analysis.
1.7 Causal Discovery in ESG Data Using Federated Learning
Background, Motivation, Problems, and Research Gaps
Identifying causal relationships in ESG data is essential for understanding how various factors, such as corporate policies or environmental initiatives, influence sustainability outcomes. Traditional methods rely on centralized data, which poses privacy concerns. Federated learning allows decentralized causal discovery, enabling multiple organizations to contribute to causal analysis without exposing sensitive data. The research gap lies in applying causal discovery algorithms within federated learning frameworks to reveal actionable ESG insights.
Research Questions
- How can causal discovery algorithms be integrated into federated learning for ESG data analysis?
- What are the key challenges in performing causal analysis on distributed data?
- How can federated causal discovery enhance our understanding of ESG factors affecting organizational performance?
Tasks
- Implement a federated learning system using Flower or PySyft.
- Integrate causal discovery algorithms (e.g., PC algorithm, GES) within the federated environment using Causal Learn library.
- Develop a method for sharing and aggregating causal structures among clients.
- Apply the system to ESG datasets to identify causal relationships and validate findings.
Ideal for: Students with an interest in Python, causal AI, and federated learning techniques applied to ESG analysis.
1.8 Integrating LLMs into Federated Learning for Automated ESG Report Generation
Background, Motivation, Problems, and Research Gaps
ESG reports provide critical insights into a company’s sustainability performance, but their generation involves complex data processing and a careful balance between data utility and privacy. Traditional methods often involve manual data compilation and risk of data exposure. Integrating Large Language Models (LLMs) like GPT into federated learning frameworks can automate the generation of these reports while preserving data privacy. However, there is a gap in research focusing on how LLMs can be effectively fine-tuned within federated environments for comprehensive and secure ESG reporting.
Research Questions
- How can LLMs be integrated into a federated learning setup to facilitate automated ESG report generation?
- What privacy and data security challenges arise when using federated learning for ESG data?
- How does the quality of ESG reports generated by federated LLMs compare to those generated through traditional methods?
Tasks
- Set up a federated learning environment using a framework like Flower or PySyft.
- Integrate a pre-trained LLM (e.g., BERT, GPT) into this federated setup.
- Develop a pipeline for ESG data collection, processing, and automated report generation.
- Evaluate the generated reports for quality, privacy, and accuracy in comparison with traditional methods.
Ideal for: Students proficient in Python, interested in federated learning, NLP, and data privacy in ESG reporting.
1.9 LLM-Assisted Collection of ESG Data from News Articles and Media Sources
Background, Motivation, Problems, and Research Gaps
ESG performance is not only reflected in company reports but also in media coverage, news articles, and other public sources. Collecting and analyzing this external data is essential for a holistic ESG assessment, but manual data collection is resource-intensive. Large Language Models (LLMs) can assist in processing vast amounts of text data, extracting relevant ESG-related information from diverse media sources. However, applying LLMs for this specific purpose remains relatively unexplored, representing a research gap this study seeks to address.
Research Questions
- How can LLMs be used to automatically collect and extract ESG-related information from news articles and media sources?
- What are the limitations and challenges in using LLMs for identifying ESG topics in diverse media content?
- How effective is LLM-based data collection compared to manual methods?
Tasks
- Conduct a literature review on the use of LLMs in text analysis and media monitoring.
- Utilize pre-trained LLMs to collect ESG-related information from a set of news articles and media sources.
- Analyze the results to identify trends, key themes, and the overall effectiveness of LLM-assisted data collection.
- Compare the LLM-generated data with manually collected data to evaluate accuracy and comprehensiveness.
Ideal for: Students interested in ESG assessments, media analysis, and applying natural language processing tools, requiring minimal programming skills.
1.10 LLMs for Extracting Key ESG Indicators from Public Reports
Background, Motivation, Problems, and Research Gaps
Publicly available ESG reports contain crucial information on various indicators like carbon footprint, diversity metrics, and waste management. However, manually extracting specific indicators from these extensive documents is time-intensive. Large Language Models (LLMs) have the potential to automate this extraction process, but their application in efficiently identifying and collecting key ESG indicators is underexplored. This study aims to address this gap by utilizing LLMs to streamline ESG data collection.
Research Questions
- How can LLMs be applied to identify and extract key ESG indicators from publicly available reports?
- What specific challenges do LLMs face when extracting diverse indicators from various reporting formats?
- How effective are LLMs in extracting ESG indicators compared to traditional manual methods?
Tasks
- Review literature on LLM usage for text extraction and analysis, particularly in ESG contexts.
- Apply pre-trained LLMs to a sample set of ESG reports to identify and extract key indicators.
- Compare the extraction results from LLMs with those obtained through manual methods.
- Analyze the effectiveness and accuracy of LLMs in automating ESG data collection.
Ideal for: Students interested in sustainability data analysis and text processing, with a focus on using LLMs and minimal programming.
1.11 Exploring Causal Reinforcement Learning to Enhance Sustainability in Multimodal City Logistics
Background, Motivation, Problems, and Research Gaps:
City logistics is increasingly becoming a complex challenge as urban areas expand, leading to higher traffic congestion, pollution, and inefficiencies in goods delivery. Traditional logistics models often struggle to adapt to the dynamic nature of cities, particularly when multiple transportation modes (e.g., trucks, bicycles, electric vehicles) are involved. Ensuring sustainability in multimodal city logistics requires understanding the causal factors affecting transportation efficiency, environmental impact, and resource allocation.
Causal reinforcement learning (RL) offers a promising approach to address this challenge. By incorporating causal knowledge into decision-making processes, RL can adapt to changing urban environments and make informed, sustainable logistics decisions. However, research on applying causal reinforcement learning specifically to multimodal city logistics is limited, leaving a gap in exploring how this advanced technique can optimize logistics systems for better sustainability outcomes.
Research Questions:
- How can causal reinforcement learning be applied to improve the sustainability of multimodal city logistics?
- What are the key causal factors affecting the efficiency and environmental impact of different transportation modes in urban logistics?
- How does incorporating causal relationships in reinforcement learning models influence decision-making and system performance in city logistics?
Tasks:
- Conduct a literature review on multimodal city logistics and the use of causal reinforcement learning in transportation and logistics systems.
- Identify key sustainability factors in city logistics, such as emissions, energy consumption, and delivery times, and explore their causal relationships.
- Develop a causal reinforcement learning model that incorporates these relationships to optimize decisions in a simulated multimodal city logistics environment.
- Evaluate the model’s performance in terms of sustainability outcomes (e.g., reduced emissions, improved efficiency) and compare it to traditional logistics models.
- Analyze the results to identify the strengths, limitations, and potential areas for improvement in using causal reinforcement learning for sustainable city logistics.
Ideal for:
Students interested in sustainability, urban logistics, and the application of advanced AI techniques. This topic is particularly suitable for those with a background in data science, machine learning, and an understanding of reinforcement learning concepts. Basic programming skills (e.g., Python) and experience with simulation environments or logistics models would be advantageous.
2 Topics Related to Survey and Interview and Data Analysis
These topics involve several key steps:
- Conducting a literature review to identify essential variables and factors.
- Developing a hypothesis model to establish relationships between the identified variables.
- Collecting data through public surveys or expert interviews/surveys.
- Performing quantitative analysis of the survey/interview results using statistical methods, Multi-Criteria Decision Making (MCDM) techniques, or Large Language Models (LLMs).
These topics are well-suited for students interested in the complete quantitative research process, from hypothesis development and data collection to hypothesis testing and data analysis. Depending on the chosen data analysis method, programming skills may not be required.
2.1 Causal Analysis of Digital Product Passports on Consumer Purchasing Behavior
Background, Motivation, Problems, and Research Gaps:
Digital Product Passports (DPPs) are emerging as a vital tool for promoting transparency and sustainability in consumer products. These passports provide detailed information on a product’s origin, materials, manufacturing processes, and environmental impact, enabling consumers to make more informed purchasing decisions. While DPPs have gained attention for their potential to influence consumer behavior towards more sustainable choices, the actual causal impact of DPPs on purchasing decisions remains unclear.
Existing research primarily focuses on the descriptive and predictive aspects of DPPs without exploring the causal relationships between the availability of product information and changes in consumer behavior. This thesis aims to fill this gap by applying causal analysis to understand how DPPs affect purchasing patterns, revealing the key factors that drive consumer decision-making in the context of sustainability.
Research Questions:
- What specific information in DPPs (e.g., product origin, environmental impact) influence on consumer decisions?
- What external factors influence on consumer decisions?
- How does the presence of DPPs affect consumers’ willingness to pay for sustainable products?
Tasks:
- Conduct a literature review on Digital Product Passports and their role in sustainable consumer behavior.
- Identify key variables in DPPs (e.g., product lifecycle information, environmental footprint) that may influence purchasing decisions.
- Develop hypotheses.
- Collect and preprocess data through public surveys.
- Apply causal analysis methods (e.g., Structural Equation Modeling, Causal Inference) to identify and quantify the causal impact of DPPs on consumer purchasing behavior.
- Analyze the results to identify which aspects of DPPs are most influential in driving sustainable consumer choices and discuss potential implications for manufacturers and policymakers.
Ideal for:
Students interested in sustainability, consumer behavior, and data analysis. This topic is particularly suitable for those with a background in industrial engineering and management, or data science and a keen interest in applying causal analysis techniques. Basic knowledge of statistical analysis, causal inference methods, and experience with data analysis tools (e.g., SmartPLS, Python) will be advantageous.
2.2 Assessing the Critical Factors for Digital Product Passport Adoption in the Circular Economy Using Multicriteria Decision Making (MCDM)
Background, Motivation, Problems, and Research Gaps
Digital product passports are critical for implementing circular economy practices, yet their adoption is influenced by various factors, such as technological capability, regulatory compliance, and market demand. There is a research gap in systematically evaluating these factors to guide stakeholders in making informed adoption decisions.
Research Questions
- What are the critical factors influencing the adoption of digital product passports within the circular economy?
- How can the TOPSIS method be used to rank these factors to facilitate decision-making?
Tasks
- Perform a literature review to identify factors affecting DPP adoption in the circular economy.
- Develop a TOPSIS model to rank these factors.
- Conduct expert survey/interview.
- Analyze the results to identify the most influential factors driving DPP adoption.
- Provide recommendations for policymakers and industry practitioners based on the TOPSIS ranking.
Ideal for: Students interested in sustainability, decision-making models, and the implementation of digital product solutions using MCDM method such as TOPSIS, DEMATEL, and AHP
These topics emphasize analytical approaches using decision-making models, causal analysis, and systematic literature reviews, making them suitable for students with limited programming skills.
3 Topics Related to Mathematical Modelling, Operation Research, and Simulation
The topics in this category involve capturing real-world problems and representing them using mathematical or formal models, such as Mixed-Integer Linear Programming (MILP), Linear Programming (LP), dynamic programming, reinforcement learning, and others. This process includes simulating the models with real data and conducting sensitivity analysis. These topics are well-suited for students with a strong interest in applying mathematical modeling to address real-world challenges. Depending on the technique applied, programming skill at the beginner level may be required.
3.1 Enhancing Sustainability in Multimodal City Logistics through Reinforcement Learning
Background, Motivation, Problems, and Research Gaps:
Urban areas are facing increasing challenges in managing city logistics due to rapid population growth, rising e-commerce demand, and the need for sustainable transportation. Multimodal logistics, which involves the use of various transportation modes (e.g., trucks, bicycles, electric vehicles), presents a viable solution. However, optimizing these systems for sustainability is complex due to the dynamic nature of urban environments, fluctuating demand, and the interplay between different transportation options.
Traditional logistics models often lack the adaptability required to respond to these changing conditions effectively. Reinforcement learning (RL) offers a promising approach by allowing logistics systems to learn and adapt to real-time changes, improving decision-making, efficiency, and sustainability. Despite its potential, research on applying RL specifically to the sustainability challenges in multimodal city logistics is limited, highlighting a gap in understanding how RL can be effectively used to optimize these complex systems.
Research Questions:
- How can reinforcement learning be applied to optimize multimodal city logistics for improved sustainability?
- What are the key factors that influence sustainability in multimodal logistics, and how can RL be used to address them?
- How does the performance of an RL-based logistics model compare to traditional logistics optimization methods in urban settings?
Tasks:
- Conduct a literature review on multimodal city logistics and the application of reinforcement learning in logistics and transportation systems.
- Identify key sustainability factors in city logistics, such as emissions, energy consumption, delivery times, and their interaction in a multimodal context.
- Develop a reinforcement learning model tailored to optimize decision-making in a simulated multimodal city logistics environment.
- Evaluate the model’s performance, focusing on its impact on sustainability outcomes (e.g., reducing emissions, improving delivery efficiency) compared to traditional logistics models.
- Analyze the results to identify the strengths, limitations, and potential areas for future research in applying RL to city logistics.
Ideal for:
Students passionate about sustainability, urban logistics, and the application of AI and machine learning. This thesis is particularly suited for those with a background in data science, machine learning, and an understanding of reinforcement learning concepts. Basic programming skills (e.g., Python) and experience with simulation tools or logistics models would be beneficial.
3.2 Optimizing Production Planning for Circular Economy Integration: A Mathematical Modeling Approach
Background, Motivation, Problems, and Research Gaps:
The circular economy is a sustainable approach that focuses on minimizing waste, promoting recycling, and extending the lifecycle of products and materials. In the manufacturing sector, integrating circular economy principles into production planning can significantly reduce resource consumption, enhance environmental sustainability, and improve cost-efficiency.
Traditional production planning models primarily focus on linear processes, from material procurement to product disposal. However, they often overlook opportunities for recycling, reuse, remanufacturing, and waste minimization. This creates a research gap in developing production plans that account for the closed-loop nature of material flows inherent in a circular economy.
This thesis aims to bridge this gap by developing a mathematical optimization model that integrates circular economy concepts into production planning. The goal is to create a model that optimizes production while incorporating aspects like material reuse, recycling, remanufacturing, and waste reduction.
Research Questions:
- How can circular economy principles be effectively integrated into production planning using mathematical optimization models?
- What are the key factors and constraints that need to be considered when developing a production plan aligned with circular economy objectives?
- How does incorporating circular economy concepts into production planning impact resource utilization, cost-efficiency, and waste reduction?
Tasks:
- Conduct a literature review on circular economy principles and existing production planning models.
- Identify key circular economy practices (e.g., recycling, reuse, remanufacturing) and incorporate them into the model’s design.
- Develop a mathematical optimization model for production planning that includes variables and constraints related to circular economy activities.
- Implement the model using optimization tools (e.g., Python with PuLP or Gurobi), and test it with different scenarios to evaluate its impact on resource efficiency and sustainability.
- Analyze the results to identify how integrating circular economy practices affects production outcomes and sustainability goals.
Ideal for:
Students with an interest in sustainable manufacturing, production planning, and operations research. This thesis is particularly suited for those who enjoy mathematical modeling and optimization and want to explore its application in promoting circular economy practices. Basic knowledge of optimization methods and familiarity with programming tools (e.g., Python, MATLAB) will be advantageous.
3.3 Integrating Circular Economy in Production Planning: A Reinforcement Learning Approach
Background, Motivation, Problems, and Research Gaps:
The circular economy emphasizes resource efficiency, waste minimization, and the reuse and recycling of materials to create a more sustainable production process. In manufacturing, incorporating circular economy principles into production planning can significantly reduce environmental impact while improving operational efficiency. However, traditional production planning methods often focus on linear processes and lack the flexibility to adapt to dynamic, closed-loop material flows inherent in a circular economy.
Reinforcement Learning (RL) offers a promising solution to this challenge. By enabling a system to learn and adapt based on real-time feedback, RL can optimize production decisions while considering complex factors like material reuse, recycling rates, and fluctuating demand. Despite its potential, research on applying RL to integrate circular economy concepts into production planning is limited, leaving a gap in developing adaptive, data-driven planning strategies that align with sustainability goals.
Research Questions:
- How can reinforcement learning be used to integrate circular economy principles into production planning?
- What key factors (e.g., recycling rates, material availability) should be considered in an RL-based production planning model for a circular economy?
- How does the performance of an RL-based production planning approach compare to traditional planning methods in terms of sustainability and efficiency?
Tasks:
- Conduct a literature review on circular economy practices and the application of reinforcement learning in production planning.
- Identify key elements of circular economy integration, such as recycling, remanufacturing, and waste reduction, to be included in the RL model.
- Develop a reinforcement learning model for production planning that dynamically adapts to changes in material availability, demand, and recycling rates.
- Implement the model in a simulated production environment using tools like Python (e.g., TensorFlow, PyTorch) to train the RL agent.
- Evaluate the model’s performance by comparing it with traditional production planning methods, focusing on sustainability outcomes (e.g., reduced waste, improved resource efficiency).
- Analyze the results to identify the strengths, limitations, and potential improvements of using RL for circular economy-driven production planning.
Ideal for:
Students interested in sustainable manufacturing, operations research, and the application of artificial intelligence in production planning. This thesis is particularly suited for those with a background in machine learning, data science, or reinforcement learning, and who are eager to explore advanced, adaptive decision-making techniques. Basic programming skills (preferably in Python) and familiarity with RL concepts and libraries (e.g., TensorFlow, PyTorch) will be advantageous.
4 Topics Related to Systematic Literature Review and Theoretical Model Development
The following topics focus on conducting systematic literature reviews, extracting key insights from research, and developing theoretical models. The literature review and information extraction will be enhanced using various AI tools for literature management and analysis. These thesis topics do not require programming skills but do require an interest in reading and analyzing scientific papers.
4.1 Cybersecurity Challenges in Vehicle Predictive Diagnostics Systems
Background, Motivation, Problems, and Research Gaps
As predictive diagnostic systems in vehicles become increasingly complex, cybersecurity concerns are escalating. Existing research predominantly focuses on technical aspects of these systems, while the impact of cybersecurity challenges on their development and implementation remains underexplored. The research gap lies in identifying and addressing the cybersecurity issues specific to vehicle predictive diagnostics, facilitating safer adoption and standardization in the automotive industry.
Research Questions
- What cybersecurity challenges affect the deployment of vehicle predictive diagnostics systems?
- How do these cybersecurity concerns impact the design and development of fault prediction mechanisms?
- What recommendations can be made to improve cybersecurity standards for predictive diagnostics in the automotive sector?
Tasks
- Conduct a literature analysis of cybersecurity issues in vehicle predictive diagnostics across different regions.
- Study the influence of cybersecurity on the development and implementation of fault prediction systems.
- Categorize vehicles based on their cybersecurity requirements for repairs, diagnostics, and warranties.
- Develop recommendations for enhancing cybersecurity standards in automotive predictive diagnostics.
Ideal for: Students with interests in cybersecurity, automotive technologies, and security studies.
Contact: egraha@constructor.university
4.2 Exploring Federated Learning for ESG (Sustainability) Data Sharing in Supply Chains
Background, Motivation, Problems, and Research Gaps
ESG assessments in supply chains often require sharing sensitive data, raising privacy concerns. Federated learning enables collaborative model-building without sharing raw data, making it an attractive solution. However, its application in ESG data sharing within supply chains has not been thoroughly explored, creating a research gap. This study aims to investigate the potential of federated learning for secure ESG data collaboration in multi-tier supply chains.
Research Questions
- How can federated learning be applied to facilitate ESG data sharing in supply chains?
- What are the benefits and challenges of using federated learning for ESG assessments in supply chains?
- How does federated learning impact data privacy and collaboration among supply chain partners?
Tasks
- Review literature on federated learning and its applications in data privacy and collaborative modeling.
- Explore case studies of supply chains that would benefit from secure ESG data sharing.
- Propose a conceptual framework for using federated learning in ESG data sharing.
- Analyze the potential challenges and benefits of implementing federated learning in a supply chain context.
Ideal for: Students interested in supply chain management, data privacy, and collaborative modeling, with a focus on conceptual understanding rather than programming.
4.3 Developing a Framework for Federated Learning-Enhanced LLMs in ESG Data Assessment
Background, Motivation, Problems, and Research Gaps
Assessing ESG data accurately requires comprehensive analysis of diverse and sensitive information. LLMs have demonstrated the ability to process large volumes of textual data, but privacy concerns restrict sharing ESG data across organizations. Federated learning provides a solution by enabling collaborative model training without exposing raw data. There is a research gap in creating a standardized framework that combines federated learning and LLMs for multi-organizational ESG assessment while ensuring data privacy and quality.
Research Questions
- What are the key components of a framework that integrates federated learning and LLMs for ESG data assessment?
- How can federated learning enhance LLM-based ESG assessments in a multi-organizational environment?
- What challenges need to be addressed when developing such an integrated framework?
Tasks
- Conduct a literature review on federated learning and LLM applications in ESG data assessment.
- Identify the key components required for integrating federated learning with LLMs.
- Develop a conceptual framework for using federated learning-enhanced LLMs for ESG assessment.
- Discuss the benefits, potential challenges, and limitations of the proposed framework.
Ideal for: Students interested in data analysis, sustainability assessments, and conceptual framework development, with a focus on integrating advanced technologies without extensive programming.
4.4 Developing a Conceptual Framework for Using Large Language Models (LLMs) in Sustainability/ESG Data Collection and Report Generation: A Systematic Literature Review
Background, Motivation, Problems, and Research Gaps:
The importance of sustainability and Environmental, Social, and Governance (ESG) reporting has been growing rapidly as stakeholders demand greater transparency and accountability from organizations. However, creating comprehensive and accurate ESG reports is challenging due to the vast amount of unstructured data that must be collected, processed, and analyzed. Large Language Models (LLMs) like GPT-4 have the potential to revolutionize this process by automating data collection, extracting relevant information, and generating well-structured sustainability/ESG reports.
Despite the potential of LLMs in this context, there is a lack of research exploring a standardized approach for their application in sustainability/ESG report generation. This thesis aims to bridge this gap by conducting a systematic literature review (SLR) to develop a conceptual framework for utilizing LLMs in the data collection and report generation processes for sustainability/ESG reporting.
Research Questions:
- What current methods and practices exist for using LLMs in sustainability/ESG data collection and report generation?
- What are the key challenges and limitations of applying LLMs in the context of ESG reporting?
- How can a conceptual framework guide the use of LLMs to improve the efficiency and accuracy of sustainability/ESG data collection and report generation?
Tasks:
- Conduct a systematic literature review (SLR) to gather research on the use of LLMs for sustainability/ESG data collection and report generation.
- Analyze the findings to identify key trends, methodologies, challenges, and gaps in using LLMs for ESG reporting.
- Develop a conceptual framework outlining the processes, tools, and best practices for leveraging LLMs in collecting data and generating sustainability/ESG reports.
- Validate the conceptual framework through expert feedback or illustrative case studies (if applicable).
- Provide recommendations for future research and practical implementation of LLMs in ESG reporting.
Ideal for:
Students interested in sustainability, ESG reporting, natural language processing (NLP), and the application of AI models in data analysis and report generation. This thesis is ideal for those who are curious about exploring how advanced AI techniques can enhance sustainability practices. Familiarity with LLMs (e.g., GPT-3, GPT-4), systematic literature reviews, and report generation processes will be advantageous.
4.5 A Conceptual Framework for Integrating Domain Expertise with Data-Driven Methods in Generating Causal Models: A Systematic Literature Review
Background, Motivation, Problems, and Research Gaps:
Causal modeling is essential for understanding complex systems in fields like supply chain, economics, healthcare, and environmental science. Traditionally, causal models are developed using either domain expertise or data-driven methods such as statistical analysis and machine learning. While data-driven approaches can uncover patterns in large datasets, they often lack the nuanced insights provided by domain experts. Conversely, expert-driven models might overlook data complexities due to human limitations in processing vast information.
There is a growing need for frameworks that effectively integrate domain expertise with data-driven methods to generate more robust and accurate causal models. However, the research on combining these two approaches remains fragmented, with no standardized conceptual framework to guide the integration process. This thesis aims to address this gap by conducting a systematic literature review (SLR) to develop a comprehensive framework for combining domain knowledge with data-driven causal discovery techniques.
Research Questions:
- What existing methods and practices are used to integrate domain expertise with data-driven approaches for generating causal models?
- What challenges and limitations arise in combining expert knowledge with data-driven methods in causal discovery?
- How can a conceptual framework be developed to guide the effective integration of domain expertise and data-driven techniques in generating causal models?
Tasks:
- Conduct a systematic literature review (SLR) to identify studies and approaches that integrate domain expertise with data-driven methods for causal modeling.
- Analyze the literature to identify key trends, methodologies, challenges, and gaps in existing integration practices.
- Develop a conceptual framework that outlines the processes, tools, and best practices for combining domain knowledge with data-driven methods to generate causal models.
- Validate the proposed framework through feedback from domain experts or illustrative case studies (if applicable).
- Provide recommendations for future research and implementation strategies for integrating domain expertise in data-driven causal modeling.
Ideal for:
Students interested in causal inference, data science, and interdisciplinary approaches to model building. This thesis is particularly suitable for those who have a basic understanding of data-driven methods (e.g., machine learning, statistical analysis) and are keen on exploring how to enhance these methods using domain-specific expertise. Familiarity with systematic literature review methodologies and causal discovery techniques will be beneficial.
4.6 Data-Driven Scrap Prediction and Reduction in Manufacturing: An AI-Assisted Systematic Literature Review
Background, Motivation, Problems, and Research Gaps
Scrap and waste generation in manufacturing processes lead to significant financial and environmental costs. Reducing scrap rates is vital for enhancing productivity, ensuring product quality, and achieving sustainability goals. In recent years, data-driven techniques, particularly those using artificial intelligence (AI) and machine learning, have shown potential in predicting and reducing scrap by analyzing vast amounts of production data.
However, the research on applying AI for scrap prediction and reduction is diverse and fragmented, with varying methodologies and applications across different manufacturing contexts. Furthermore, there is a lack of comprehensive literature reviews that systematically explore how AI-driven data analytics can be leveraged to tackle scrap-related challenges. This thesis aims to fill this gap by conducting an AI-assisted systematic literature review (SLR) to identify and evaluate the use of data-driven methods for scrap prediction and reduction in manufacturing.
Research Questions:
- What AI-driven, data-driven techniques are currently used for scrap prediction and reduction in manufacturing?
- What are the key challenges and limitations in implementing these AI-based approaches in real-world manufacturing environments?
- How can the insights from the AI-assisted SLR guide the development of a framework for integrating AI in scrap reduction strategies?
Tasks:
- Utilize AI-based literature analysis tools (e.g., natural language processing, citation analysis) to identify relevant research papers on data-driven scrap prediction and reduction.
- Conduct an AI-assisted systematic literature review (SLR) to extract, categorize, and evaluate methods used for scrap prediction and reduction, focusing on AI and machine learning techniques.
- Analyze the findings to identify key trends, popular algorithms, and challenges in applying AI for scrap reduction in various manufacturing contexts.
- Develop a conceptual framework or set of best practices for implementing AI-driven scrap reduction strategies based on insights from the literature.
- Identify research gaps and provide recommendations for future work in leveraging AI for optimizing manufacturing processes.
Ideal for:
Students interested in the intersection of AI, data analytics, and manufacturing processes. This thesis is well-suited for those who have a foundational understanding of AI, machine learning, and data-driven methods, and are keen on exploring their application in industrial contexts. Familiarity with systematic literature review methodologies and an interest in applying AI tools for research analysis will be advantageous.
5 Topics Related to Software Development
The topics in this category are ideal for computer science students or those in related fields who are interested in gaining practical experience in software development and programming.
5.1 Creating a Real-Time Component Health Display
Background/Motivation/Problem
Real-time monitoring of component wear and tear is crucial for predictive maintenance, but current systems often lack a user-friendly visualization interface. The gap lies in creating a simple, yet effective display that communicates real-time health data of components to users, aiding in preemptive actions and maintenance.
Research Questions
- What are the current methods for tracking component wear in real-time?
- Which visualization techniques are most effective in displaying component health and predicting failures?
- How can a real-time health display prototype be validated for its usability and effectiveness?
Tasks
- Perform a literature review to explore existing real-time tracking systems.
- Design a user-friendly display for monitoring component conditions.
- Develop a system concept to share diagnostic information effectively.
- Implement and test a prototype, collecting feedback to evaluate its performance.
Ideal for: Students who enjoy working on user interfaces, with system design and programming skills.
Contact: egraha@constructor.university