Zhaleh Rahimi

Zhaleh Rahimi

PhD Candidate at McMaster University in Computer Science.

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

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.

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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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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