@article{ejlali_arian_taghiyeh_chambers_sadeghi_taghiye_cakdi_handfield_2024, title={Developing hybrid machine learning models to assign health score to railcar fleets for optimal decision making}, volume={250}, ISSN={["1873-6793"]}, url={https://doi.org/10.1016/j.eswa.2024.123931}, DOI={10.1016/j.eswa.2024.123931}, abstractNote={A large amount of data is generated during the operation of a railcar fleet, which can easily lead to dimensional disaster and reduce the resiliency of the railcar network. To solve these issues and offer predictive maintenance, this research introduces a hybrid fault diagnosis expert system method that combines density-based spatial clustering of applications with noise (DBSCAN) and principal component analysis (PCA). Firstly, the DBSCAN method is used to cluster categorical data that are similar to one another within the same group. Secondly, PCA algorithm is applied to reduce the dimensionality of the data and eliminate redundancy in order to improve the accuracy of fault diagnosis. Finally, we explain the engineered features and evaluate the selected models by using the Gain Chart and Area Under Curve (AUC) metrics. We use the hybrid expert system model to enhance maintenance planning decisions by assigning a health score to the railcar system of the North American Railcar Owner (NARO). The model is based on data from one specific railcar type. We utilize the results of data-driven models to assign a health rate to each railcar. Finally, we use data from NARO to evaluate and verify our proposed framework.}, journal={EXPERT SYSTEMS WITH APPLICATIONS}, author={Ejlali, Mahyar and Arian, Ebrahim and Taghiyeh, Sajjad and Chambers, Kristina and Sadeghi, Amir Hossein and Taghiye, Emad and Cakdi, Demet and Handfield, Robert B.}, year={2024}, month={Sep} }
 @article{ala_deveci_bani_sadeghi_2024, title={Dynamic capacitated facility location problem in mobile renewable energy charging stations under sustainability consideration}, volume={41}, ISSN={["2210-5387"]}, DOI={10.1016/j.suscom.2023.100954}, abstractNote={The deployment of mobile renewable energy charging stations plays a crucial role in facilitating the overall adoption of electric vehicles and reducing reliance on fossil fuels. This study addresses the dynamic capacitated facility location problem in mobile charging stations from a sustainability perspective. This paper proposes Two-stage stochastic programming with recourse that performs well for this application, and the location of the mobile renewable energy charging station (MRECS) management addresses the complex dynamics of reusable items. To solve this problem, we suggested dealing with differential evolutionary (DE) and DE Q-learning (DEQL) algorithms, as two novel optimization and reinforcement learning approaches, are presented as solution approaches to validate their performance. Evaluation of the outcomes reveals a considerable disparity between the algorithms, and DEQL performs better in solving the presented problem. In addition, DEQL could minimize the total operation cost and carbon emission by 7% and 20%, respectively. In contrast, the DE could decrease carbon emissions and total operation costs by 5% and 2.5%, respectively.}, journal={SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS}, author={Ala, Ali and Deveci, Muhammet and Bani, Erfan Amani and Sadeghi, Amir Hossein}, year={2024}, month={Jan} }
 @article{sadeghi_sun_sahebi-fakhrabad_arzani_handfield_2023, title={A Mixed-Integer Linear Formulation for a Dynamic Modified Stochastic p-Median Problem in a Competitive Supply Chain Network Design}, volume={7}, ISSN={["2305-6290"]}, url={https://doi.org/10.3390/logistics7010014}, DOI={10.3390/logistics7010014}, abstractNote={Background: The Dynamic Modified Stochastic p-Median Problem (DMS-p-MP) is an important problem in supply chain network design, as it deals with the optimal location of facilities and the allocation of demand in a dynamic and uncertain environment. Methods: In this research paper, we propose a mixed-integer linear formulation for the DMS-p-MP, which captures the key features of the problem and allows for efficient solution methods. The DMS-p-MP adds two key features to the classical problem: (1) it considers the dynamic nature of the problem, where the demand is uncertain and changes over time, and (2) it allows for the modification of the facility locations over time, subject to a fixed number of modifications. The proposed model uses robust optimization in order to address the uncertainty of demand by allowing for the optimization of solutions that are not overly sensitive to small changes in the data or parameters. To manage the computational challenges presented by large-scale DMS-p-MP networks, a Lagrangian relaxation (LR) algorithm is employed. Results: Our computational study in a real-life case study demonstrates the effectiveness of the proposed formulation in solving the DMS p-Median Problem. The results show that the number of opened and closed buildings remains unchanged as the time horizon increases due to the periodic nature of our demand. Conclusions: This formulation can be applied to real-world problems, providing decision-makers with an effective tool to optimize their supply chain network design in a dynamic and uncertain environment.}, number={1}, journal={LOGISTICS-BASEL}, author={Sadeghi, Amir Hossein and Sun, Ziyuan and Sahebi-Fakhrabad, Amirreza and Arzani, Hamid and Handfield, Robert}, year={2023}, month={Mar} }
 @article{sadeghi_bani_fallahi_handfield_2023, title={Grey Wolf Optimizer and Whale Optimization Algorithm for Stochastic Inventory Management of Reusable Products in a Two-Level Supply Chain}, volume={11}, ISSN={["2169-3536"]}, DOI={10.1109/ACCESS.2023.3269292}, abstractNote={Product reuse and recovery is an efficient tool that helps companies to simultaneously address economic and environmental dimensions of sustainability. This paper presents a novel problem for stock management of reusable products in a single-vendor, multi-product, multi-retailer network. Several constraints, such as the maximum budget, storage capacity, number of orders, etc., are considered in their stochastic form to establish a more realistic problem. The presented problem is formulated using a nonlinear programming mathematical model. The chance-constrained approach is suggested to deal with the constraints’ uncertainty. Regarding the nonlinearity of the model, grey wolf optimizer (GWO) and whale optimization algorithm (WOA) as two novel metaheuristics are presented as solution approaches, and the sequential quadratic programming (SQP) exact algorithm validates their performance. The parameters of algorithms are calibrated using the Taguchi method for the design of experiments. Extensive analysis is established by solving several numerical results in different sizes and utilizing several comparison measures. Also, the results are compared statistically using proper parametric and non-parametric tests. The analysis of the results shows a significant difference between the algorithms, and GWO has a better performance for solving the presented problem. In addition, both algorithms perform well in searching the solution space, where the GWO and WOA differences with the optimal solution of the SQP algorithm are negligible.}, journal={IEEE ACCESS}, author={Sadeghi, Amir Hossein and Bani, Erfan Amani and Fallahi, Ali and Handfield, Robert}, year={2023}, pages={40278–40297} }
 @article{ala_sadeghi_deveci_pamucar_2023, title={Improving smart deals system to secure human-centric consumer applications: Internet of things and Markov logic network approaches}, ISSN={["1572-9362"]}, DOI={10.1007/s10660-023-09787-1}, abstractNote={Abstract}, journal={ELECTRONIC COMMERCE RESEARCH}, author={Ala, Ali and Sadeghi, Amir Hossein and Deveci, Muhammet and Pamucar, Dragan}, year={2023}, month={Dec} }
 @article{sahebi-fakhrabad_sadeghi_kemahlioglu-ziya_handfield_tohidi_vasheghani-farahani_2023, title={The Impact of Opioid Prescribing Limits on Drug Usage in South Carolina: A Novel Geospatial and Time Series Data Analysis}, volume={11}, ISSN={["2227-9032"]}, url={https://doi.org/10.3390/healthcare11081132}, DOI={10.3390/healthcare11081132}, abstractNote={The opioid crisis in the United States has had devastating effects on communities across the country, leading many states to pass legislation that limits the prescription of opioid medications in an effort to reduce the number of overdose deaths. This study investigates the impact of South Carolina’s prescription limit law (S.C. Code Ann. 44-53-360), which aims to reduce opioid overdose deaths, on opioid prescription rates. The study utilizes South Carolina Reporting and Identification Prescription Tracking System (SCRIPTS) data and proposes a distance classification system to group records based on proximity and evaluates prescription volumes in each distance class. Prescription volumes were found to be highest in classes with pharmacies located further away from the patient. An Interrupted Time Series (ITS) model is utilized to assess the policy impact, with benzodiazepine prescriptions as a control group. The ITS models indicate an overall decrease in prescription volume, but with varying impacts across the different distance classes. While the policy effectively reduced opioid prescription volumes overall, an unintended consequence was observed as prescription volume increased in areas where prescribers were located at far distances from patients, highlighting the limitations of state-level policies on doctors. These findings contribute to the understanding of the effects of prescription limit laws on opioid prescription rates and the importance of considering location and distance in policy design and implementation.}, number={8}, journal={HEALTHCARE}, author={Sahebi-Fakhrabad, Amirreza and Sadeghi, Amir Hossein and Kemahlioglu-Ziya, Eda and Handfield, Robert and Tohidi, Hossein and Vasheghani-Farahani, Iman}, year={2023}, month={Apr} }