Shima Azizi

Shima Azizi, Ph.D., is an Assistant Professor of Business Analytics and Information Systems in the Peter J. Tobin College of Business at St. John’s University. She received her Ph.D. in Operations Management from Worcester Polytechnic Institute, her M.Sc. in Industrial Engineering from the University of Kurdistan, and her B.Sc. in Industrial Engineering from the University of Tabriz. She teaches courses in prescriptive and predictive analytics, and operations management. Her research focuses on the use of advanced analytics, specifically operations research and data science, to solve practical problems in humanitarian operations, healthcare, and sustainability. Prior to joining St. John’s, she investigated alleviating challenges within refugee camp systems, the United States foster care system, as well as the United States community paramedicine programs.

Shima Azizi
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Ph.D., Business Administration, Worcester Polytechnic Institute


M.S., Industrial Engineering, University of Kurdistan


B.S., Industrial Engineering, University of Tabriz



Aid Allocation for Camp-Based and Urban Refugees with Uncertain Demand and Replenishments

Status: Published at Production and Operations Management, 2021

Camp-based refugees seek shelter in camps, and urban refugees in nearby areas. Aid distribution to camps should prioritize camp-based refugees, yet share excess inventory with urban refugees when able. Amid uncertainty in demands and replenishments, we derive an inventory policy to govern a camp's aid sharing with urban refugees. We use the policy to construct expected costs of referring urban refugees elsewhere, depriving camp-based refugees, and holding, and embed them in a cost-minimizing aid allocation problem. Our study reveals insights into humanitarian aid allocation amid uncertainty.

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Select, Route and Schedule: Optimizing Community Paramedicine Service Delivery with Mandatory Visits and Patient Prioritization

Status: Published at Health Care Management Science, 2023

Community paramedicine is a recent healthcare innovation that enables proactive visitation of patients at home, often shortly after emergency department and hospital discharge. We establish the first optimization-based framework with the goals of increasing patient welfare, reducing readmissions and emergency department visits, and lowering hospital costs. We ensure that critically ill patients are visited, and further extend our model to determine any supplemental resources necessary to ensure feasibility. We develop a prioritization method for patient visits based on patient health features, integrating this information into our approach that prioritizes patients, schedules visits and routes healthcare providers. We develop managerial insights via computational experiments on a variety of test instances based on real data from an upstate New York hospital.

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Introduction to Business Analytics (Instructor)

Undergraduate Fall, Spring 2022-2024

This course covers three areas of Business Analytics; Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. The topic covers in this course are Descriptive Analytics, Visual Analytics, Big Data and Data Mining, Regression Analysis and Model Building, Forecasting Models, Optimization, and Simulation. It provides students with the fundamental concepts and tools needed to understand the emerging role of business analytics in organizations and shows students how to apply basic business analytics tools and how to communicate with analytics professionals to effectively use and interpret analytic models and results for making better business decision. Emphasis is placed on applications, concepts and interpretation of results.

Modern Statistics Ⅰ (Instructor)

Undergraduate Fall, Spring 2022-2024

The course discusses the introductory descriptive statistical measures and statistical theory of estimation and hypothesis testing relevant to business problems. Topics include: methods of data presentation, measures of central tendency and dispersion, probability theory, classical discrete and continuous probability distributions, linear regression analysis, correlation analysis, sampling distributions, and hypothesis testing and estimation for one population. Excel spreadsheet program would be utilized to enhance the learning process.

Practical Optimization: Methods and Applications (Teaching Assistant)

Undergraduate Fall 2018, 2020

This course covers the use of practical computational methods to solve constrained optimization problems from industry. Optimization theory and algorithms related to linear and integer programming will be discussed, with primary emphasis placed upon computationally solving applications in the industrial, operational, manufacturing, and service sectors. Both proprietary and open-source optimization software will be used, including spreadsheet solvers (e.g., Excel Solver, OpenSolver), industrial-strength optimization packages (e.g., CPLEX, GUROBI), as well as modeling languages (e.g., AMPL, OPL, GMPL). Students will be expected to model problems and interpret their results; where applicable, sensitivity analysis, duality and additional techniques will be utilized to gain managerial insight from developed models and solutions. Cases from industries such as health care, supply chain management, financial services and analytics will be used for illustrations, discussions, and exercises.

Introduction to Prescriptive Analytics (Guest Lecturer)

Undergraduate Fall, Spring 2021

This course provides an introduction to prescriptive analytics, which involves the application of mathematical and computational sciences, such as linear optimization and simulation, to recommend optimal courses of action for decision making. The course will feature decision problems arising from a variety of contexts such as capacity management, finance, healthcare, humanitarian relief, inventory management, production planning, staffing, and supply chain. The emphasis of the course is the application of such techniques to recommend a best strategy or course of action for the particular context.