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publications
The association of vitamin-D level with catheter-related-thrombosis in hemodialysis patients: A data mining model
Published in The Journal of Vascular Access, 2023
This study investigates the association of different risk factors including vitamin-D level with catheter-related-thrombosis in hemodialysis patients by applying data mining techniques.
Recommended citation: Rahimi, Z., Abdolvand, N., Sepehri, M. M., Khavanin Zadeh, M. (2023)." The association of vitamin-D level with catheter-related-thrombosis in hemodialysis patients: A data mining model." The Journal of Vascular Access, 24(4), 606-613." .
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Clustering-Based Demand Forecasting with an Application to Immunoglobulin Products
Published in Operations Research, Data Analytics and Logistics, 2025
Our study proposes an iterative clustering-based demand forecasting framework to address this issue. We cluster patients based on domain knowledge and demand pattern characteristics using the robust and sparse K-means algorithm. We then employ time-series analysis techniques to forecast demand for each cluster, aggregate the forecasts, and evaluate the performance. The potential variables affecting the clustering and forecasting results are identified to make this process iterative and to find the best clustering scheme based on forecast performance. For example, the optimal number of clusters, K, in a K-means algorithm is unknown. Therefore, we choose K to optimize the forecast performance. Clustering algorithms can also be sensitive to feature selection, so using an extension of K-means with weighted features, the bound on feature weights is included as an unknown input variable in the iterative process. We further enhance the forecasting model by incorporating individual patient-level predictions from the cluster identified with extended treatment plans, which contains patients with more data points and better individual predictability. The proposed framework outperforms baseline ARIMA and LSTM network models trained on aggregate demand data. Moreover, the results show improved performance as data size increases. While we implemented this approach to forecast short-term demand for immunoglobulin products, we also discussed its potential applicability in areas such as supply chain management.
Recommended citation: Rahimi, Z., Li, N., Down, D. G., Arnold, D. (2025)." Clustering-based demand forecasting with an application to immunoglobulin products." Operations Research, Data Analytics and Logistics.
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Ordering Policies for Multi-Item Inventory Systems with Correlated Demands
Published in European Journal of Operational Research, 2025
This paper investigates optimal ordering policies for a multi-item periodic-review inventory system, considering demand correlations and historical data for the products involved. We extend inventory models by transitioning from an autoregressive moving average (ARMA) demand process to a vector autoregressive moving average (VARMA) framework, explicitly characterizing optimal ordering policies when there is both autocorrelation and cross-correlation among multiple items. Through experimental studies, we evaluate inventory costs and cost improvements compared to multi-item ordering policies where demands are assumed to be independent under different degrees of correlation, noise levels, and training data window sizes. The results show that, for moderate to high levels of dependence among products, the proposed framework can meaningfully decrease inventory costs. Additionally, we apply our findings to real-world data to optimize inventory policies for immunoglobulin sub-products, intravenous (IVIg) and subcutaneous (SCIg). The results of the case study also show cost improvements using the proposed policy.
Recommended citation: Rahimi, Z., Down, D. G., Li, N., Arnold, D. (2025)." Ordering Policies for Multi-Item Inventory Systems with Correlated Demands." European Journal of Operational Research.
talks
Clustering-based Demand Forecasting
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Optimal Ordering Policies for Multi-item Inventory Systems: A Case Study on Immunoglobulin Products
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teaching
C Programming
Undergraduate course, McMaster University, Engineering, 2022
Delivered lab instruction on UNIX systems, C programming, testing, profiling, benchmarking, and revision control using Git. Guided students through real-world software development practices, including shell scripting, debugging, and documentation.
Scientific Computation
Undergraduate course, McMaster University, Engineering, 2023
Facilitated tutorials and assessments on topics such as numerical methods, interpolation, differential equations, and eigenvalue problems.
Performance Analysis of Computer Systems
Undergraduate course", McMaster University, Department of Computing and Software, 2024
Led tutorials and graded coursework for subjects including Markov processes (CTMC, DTMC), queuing models, and simulation-based performance evaluation of computer networks.
Applications of Machine Learning
Undergraduate course, McMaster University, Department of Computing and Software, 2024
Delivered tutorials and graded assignments on key machine learning topics including supervised, unsupervised (clustering), reinforcement learning, fairness and bias, neural networks, computer vision, and natural language processing (NLP). Designed hands-on programming demos using “PyTorch, TensorFlow, and Keras” in Python, helping students apply theoretical concepts to real-world problems.
Performance Analysis of Computer Systems
Undergraduate course", McMaster University, Department of Computing and Software, 2025
Led tutorials and graded coursework for subjects including Markov processes (CTMC, DTMC), queuing models, and simulation-based performance evaluation of computer networks.
Scientific Computation
Undergraduate course, McMaster University, Engineering, 2025
Facilitated tutorials and assessments on topics such as numerical methods, interpolation, differential equations, and eigenvalue problems.