Evaluating cyber security Assessment Models: studying the effectiveness and limitations of existing models.

• Bachelor
• Master students

• Dacorogna, M., Debbabi, N. and Kratz, M. (2023) Building up cyber resilience by better grasping cyber risk via a new algorithm for modeling heavy-tailed data, European Journal of Operational Research. Available at: https://www.sciencedirect.com/science/article/pii/S0377221723003466 (Accessed: 18 November 2024).
• Paul, J.A. and Zhang, M. (2020) Decision support model for cybersecurity risk planning: A two-stage stochastic programming framework featuring firms, government, and attacker, European Journal of Operational Research. Available at: https://www.sciencedirect.com/science/article/pii/S0377221720307992 (Accessed: 18 November 2024).

• Understanding of mathematical modeling and statistics

Quantitative Analysis of Monetary Cyber Risk Assessment Models in Hospitals.

• Bachelor
• Master students

• Kim, S. and Weber, S. (2021) Simulation methods for robust risk assessment and the distorted mix approach, European Journal of Operational Research. Available at: https://www.sciencedirect.com/science/article/pii/S0377221721006007 (Accessed: 14 November 2024).
• Malamas, V. and Chantzis, F. (no date) Risk assessment methodologies for the Internet of Medical Things: A Survey and Comparative Appraisal | IEEE Journals & Magazine | IEEE Xplore, IEEE Access. Available at: https://ieeexplore.ieee.org/abstract/document/9373445 (Accessed: 14 November 2024).
• Ampel, B. M. et al. (2024) ‘Improving Threat Mitigation Through a Cybersecurity Risk Management Framework: A Computational Design Science Approach’, Journal of Management Information Systems, 41(1), pp. 236–265. doi: 10.1080/07421222.2023.2301178.

• Understanding of mathematical equations, statistics
• any programming language.

Synthetic data generation methods for hospital operations

Contact Person: Gabriela Ciolacu
Target:

• Bachelor students.
• For Master students, we would require additional numerical investigation(s):
  
  Motivation: Synthetic data generation has emerged as a promising alternative to overcome data scarcity and privacy challenges in the healthcare sector. Synthetic healthcare data is used in multiple disciplines (e.g., computer science, public policy) for diverse purposes, such as training machine learning models for use cases where data about a particular incident (e.g., a disaster, a rare variation of COVID-19) is limited.
  
  In operations research (OR), synthetic healthcare data is still novel. However, synthetic data is not used to assess and evaluate the impact of an intervention (e.g., a new inventory management policy or a new plan for emergency response in case of a disaster). Such purpose is rather reserved for simulations, and simulations can be used to produce synthetic data. In OR, the goal of synthetic data is to evaluate and validate the performance of an optimization algorithm (e.g., for a hospital or medical unit). An exemplary OR use case focuses on generating hospital operational data to train a model. We propose this thesis to better understand its uses for OR and subsequent generation methods.
  
  This thesis aims to synthesize literature on the available syntenic data generation use cases and methods that can be used to replicate hospital operational data, such as resource distribution. Exemplary resources can be nurses, surgeons, or surgical supplies. Based on the generated synthesis, it is also expected to showcase means to evaluate the validity of such synthetic data in each OR use case context.
  
  Operations research related literature:
• Brailsford, S. C., Eldabi, T., Kunc, M., Mustafee, N., & Osorio, A. F. (2019). Hybrid simulation modelling in operational research: A state-of-the-art review. European Journal of Operational Research, 278(3), 721-737.
• Cai, Y., Song, H., & Wang, S. (2024). Managing appointment-based services with electronic visits. European Journal of Operational Research, 315(3), 863-878. – Example of synthetic data generation for  OR purposes
• Chan, K. C., Rabaev, M., & Pratama, H. (2022). Generation of synthetic manufacturing datasets for machine learning using discrete-event simulation. Production & Manufacturing Research, 10(1), 337-353. – Example of synthetic data generation for a manufacturing plant
  
  Healthcare related literature:
• Giuffrè, M., & Shung, D. L. (2023). Harnessing the power of synthetic data in healthcare: innovation, application, and privacy. NPJ digital medicine, 6(1), 186.
• Gonzales, A., Guruswamy, G., & Smith, S. R. (2023). Synthetic data in health care: A narrative review. PLOS Digital Health, 2(1), e0000082. – This is an introductory literature review about possible cases of use for synthetic healthcare data.
• Mosquera, L., El Emam, K., Ding, L., Sharma, V., Zhang, X.H., Kababji, S.E., Carvalho, C., Hamilton, B., Palfrey, D., Kong, L. and Jiang, B. (2023). A method for generating synthetic longitudinal health data. BMC Medical Research Methodology, 23(1), p.67.
  
  Requirements:
• Desired understanding of at least one programming language (e.g., Python). Not required.

Healthcare Network Resilience: Definition and Quantification

Contact Person: Gabriela Ciolacu
Target:

• Bachelor students.
• For Master students, we would require additional numerical investigation(s).

Motivation: Hospitals and healthcare networks are designed to be subject to uncertainty and withstand it. Considering hospitals' vital role in managing uncertainty, especially when addressing disasters, developing a resilience definition and quantification could assist in monitoring and ultimately preventing disastrous outcomes.

Defining and quantifying resilience remains a debate in multiple disciplines, including operations research, especially when considering the multitudes of components contributing to a system’s resilience, such as robustness or rapidity. As a concept, resilience is also context-dependent. Hence, the objective prescribed by a resilient hospital network might differ from that desired by a resilient urban network (e.g., a network of buildings).

This thesis aims to synthesize literature on the available definitions of resilience and respective quantifications, focusing on healthcare. This thesis is centered around quantifying resilience with a focus on operations research and, namely, on operational resilience. Based on the generated synthesis, the methods (e.g., indicators, optimization) used to measure and enhance the resilience of a healthcare system are also expected to be showcased and compared.

Operations research related literature:

• Ganin, A.A., Massaro, E., Gutfraind, A., Steen, N., Keisler, J.M., Kott, A., Mangoubi, R. and Linkov, I., (2016). Operational resilience: concepts, design and analysis. Scientific reports, 6(1), pp.1-12.

Healthcare related literature:

• Fallah-Aliabadi, S., Ostadtaghizadeh, A., Ardalan, A., Fatemi, F., Khazai, B., & Mirjalili, M. R. (2020). Towards developing a model for the evaluation of hospital disaster resilience: a systematic review. BMC health services research, 20, 1-11.

Modeling and Predicting Hospital Demand

Contact Person: Gabriela Ciolacu
Target:

• For bachelor students, we require a literature review (e.g., structured or unstructured).  A numerical investigation is also desired, but it is not compulsory.
• For Master students, we would require additional numerical investigation(s).  For instance, it is expected of the student to model the demand in a hospital, based on an identified dataset (see possible datasets: https://www.kaggle.com/discussions/general/168211) and aim to predict the demand following the findings of the literature review. It is expected to showcase and compare 3+ forecasting methods.

Motivation:

Hospitals are often subject to uncertainty, such as natural or human-caused disasters. Hence, accurately predicting hospital demand could assist in better preparedness against such uncertainty, such as allocating resources to avoid possible stock breakdowns or overstocking, ensuring resource availability at reasonable costs within the budget, and providing continuous patient care. However, predicting stochastic demand under uncertainty is not a simple task. For instance, surgeries in a hospital can run longer than expected due to unforeseeable complications from the patient’s conditions, or staffing or equipment may become limited due to unexpected mass casualties. For other medical units, this might not be the case. Such variability makes it challenging for decision-makers to strategically plan and budget their expenses, respond to seasonal trends, or aim to optimize resources.

This thesis aims to synthesize literature on the available methods to predict hospital demand. For instance, it is expected to deliver an overview of the predictive methods per hospital unit (e.g., operating room, emergency department) that include uncertainty and those that do not consider it (e.g., differentiate between deterministic and stochastic demand modeling). This thesis must be centered around hospital demand in uncertain scenarios (e.g., before a disaster), focusing on operations research. Based on the generated synthesis, an analysis of the methods is expected. For example, the thesis will also offer a critical analysis of use cases, specific objectives, the benefits of those methods for the applied use cases, performance measures, and limitations.

Literature:

• Barros, O., Weber, R., & Reveco, C. (2021). Demand analysis and capacity management for hospital emergencies using advanced forecasting models and stochastic simulation. Operations Research Perspectives, 8, 100208.
• Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
• Gul, M., & Celik, E. (2020). An exhaustive review and analysis on applications of statistical forecasting in hospital emergency departments. Health Systems, 9(4), 263-284.

Requirements:

• Solid understanding of time series analysis and forecasting.
• Desired knowledge in machine learning.

Multiobjective Vulnerability Assessment of Networks under Joint Node and Link Attacks

Contact Person: Stephan Helfrich
Target

1. Master students with Background in Optimization Theory

Motivation: Critical infrastructures, such as the information technology systems of hospitals or transportation networks, are highly vulnerable to malicious attacks and natural disasters. Therefore, it is essential to identify critical components whose removal or attack would severely disrupt the system. This can be achieved by formulating and solving the β-disruptor problem, an optimization problem that aims to identify a minimum-cost set of links and nodes in a network whose removal would disrupt network connectivity up to the threshold β.

The goal of this thesis is to study the multiobjective variant of the β-disruptor problem, where the connectivity threshold β is treated as the second objective. This approach provides insights into the trade-offs between vulnerability and costs and reveals additional structural insights into the interdependencies within the critical infrastructure.

Subject of studies are but not limited to:

• Literature review of existing approaches to the β-disruptor problem and its variants
• Complexity analysis of the multiobjective disruptor problem with respect to, for example, tractability and output-sensitivity
• Design of multiobjective exact algorithms
• Design of multiobjective approximation methods with worst-case guarantees on running time and solution quality
• Design of multiobjective heuristic methods
• Implementation and performance analysis of derived methods

Literature:

β-disruptor problem:

• T. N. Dinh and M. T. Thai (2015) Network Under Joint Node and Link Attacks: Vulnerability Assessment Methods and Analysis. IEEE/ACM Transactions on Networking 23:3:1001-1011
• M. Lalou, M. Tahraoui, H. Kheddouci (2018) The Critical Node Detection Problem in networks: A survey. Computer Science Review 28:92-117

Multiobjective optimization:     

• Ehrgott, M. (2008) Multicriteria Optimization (Springer Berlin, Heidelberg)
• A. Herzel, S. Ruzika, and C. Thielen (2021) Approximation Methods for Multiobjective Optimization Problems: A Survey. INFORMS Journal on Computing 33(4):1284-1299.
• Bökler F., Ehrgott M., Morris C., Mutzel P. (2017) Output-sensitive complexity of multiobjective combinatorial optimization. Journal of Multi-Critiria Decision Analysis 24: 25–36.

Requirements:

• Master students with Background in Optimization and basic complexity theory