Peer-reviewed Journal Articles

S Evaluating Nurse Staffing Levels in Perianesthesia Care Units Using Discrete Event Simulation

Leave a Comment / Health, Peer-reviewed Journal Articles, Publications /

Siddiqui, S., Morse, E., Levin, S., ( 2017 )

Evaluating Nurse Staffing Levels in Perianesthesia Care Units Using Discrete Event Simulation

IISE Transactions on Healthcare Systems Engineering, 7(4): 215-223.

Edit PDF link hereIcon

S Evaluating Nurse Staffing Levels in Perianesthesia Care Units Using Discrete Event Simulation Read More »

Tak Improving health systems performance in low- and middle-income countries: a system dynamics model of the pay-for-performance initiative in Afghanistan

Leave a Comment / Display on Homepage, Health, Peer-reviewed Journal Articles, Publications /
O Alonge, S Lin, T Igusa, DH Peters ( 2017 )

Improving health systems performance in
low- and middle-income countries: a system
dynamics model of the pay-for-performance
initiative in Afghanistan

Health Policy and Planning
Edit PDF link hereIcon

Tak Improving health systems performance in low- and middle-income countries: a system dynamics model of the pay-for-performance initiative in Afghanistan Read More »

Tak Examining the structure and behavior of Afghanistan’s routine childhood immunization system using system dynamics modeling

Leave a Comment / Health, Peer-reviewed Journal Articles, Publications /
Holly B Schuh, Maria W Merritt, Takeru Igusa, Bruce Y Lee, David H Peters ( 2017 )

Examining the structure and behavior of Afghanistan’s routine childhood immunization system using system dynamics modeling

International Journal of Health Governance
Edit PDF link hereIcon

Tak Examining the structure and behavior of Afghanistan’s routine childhood immunization system using system dynamics modeling Read More »

S Evaluating the role of electricity storage by considering short-term operation in long-term planning

Energy, Peer-reviewed Journal Articles, Publications /

Brijs, T., van Stiphout, A., Siddiqui, S., Belmans, R., ( 2017 )

Evaluating the role of electricity storage by considering short-term operation in long-term planning

Sustainable Energy, Grids and Networks, 10: 104-117

Edit PDF link hereIcon

S Evaluating the role of electricity storage by considering short-term operation in long-term planning Read More »

Tak Taking dietary habits into account: A computational method for modeling food choices that goes beyond price

Leave a Comment / Health, Peer-reviewed Journal Articles, Publications /
Rahmatollah Beheshti, Jessica C Jones-Smith, Takeru Igusa ( 2017 )

Taking dietary habits into account: A
computational method for modeling food
choices that goes beyond price

PloS one
Edit PDF link hereIcon

Tak Taking dietary habits into account: A computational method for modeling food choices that goes beyond price Read More »

S Analysis of the global wood‐chip trade’s response to renewable energy policies using a spatial price equilibrium model

Leave a Comment / Energy, Peer-reviewed Journal Articles, Publications, Transportation /

Jiang, W., Searle, S., & Siddiqui, S., ( 2017 )

Analysis of the global wood‐chip trade’s response to renewable energy policies using a spatial price equilibrium model

Biofuels, Bioproducts and Biorefining, 11(3): 505-520

Edit PDF link hereIcon

S Analysis of the global wood‐chip trade’s response to renewable energy policies using a spatial price equilibrium model Read More »

G Vector status of Aedes species determines geographical risk of autochthonous Zika virus establishment

Leave a Comment / Display on Homepage, Health, Peer-reviewed Journal Articles, Publications, Transportation /

Lauren Gardner, Nan Chen, Sahotra Sarkar ( 2017 )

Vector status of Aedes species determines geographical risk of autochthonous Zika virus establishment

PLoS neglected tropical diseases
Edit PDF link hereIcon

G Vector status of Aedes species determines geographical risk of autochthonous Zika virus establishment Read More »

Assume that a decision-maker’s uncertain behavior is observed. We develop a an inverse optimization framework to impute an objective function that is robust against misspecifications of the behavior. In our model, instead of considering multiple data points, we consider an uncertainty set that encapsulates all possible realizations of the input data. We adopt this idea from robust optimization, which has been widely used for solving optimization problems with uncertain parameters. By bringing robust and inverse optimization together, we propose a robust inverse linear optimization model for uncertain input observations. We aim to find a cost vector for the underlying forward problem such that the associated error is minimized for the worst-case realization of the uncertainty in the observed solutions. That is, such a cost vector is robust in the sense that it protects against the worst misspecification of a decision-maker’s behavior. 

As an example, we consider a diet recommendation problem. Suppose we want to learn the diet patterns and preferences of a specific person and make personalized recommendations in the future. The person’s choice, even if restricted by nutritional and budgetary constraints, may be inconsistent and vary over time. Assuming the person’s behavior can be represented by an uncertainty set, it is important to find a cost vector that renders the worst-case behavior within the uncertainty set as close to optimal as possible. Note that the cost vector can have a general meaning and may be interpreted differently depending on the application (e.g., monetary cost, utility function, or preferences). Under such a cost vector, any non-worst-case diet will thus have a smaller deviation from optimality.  

We introduce a new approach that combines inverse optimization with conventional data analytics to recover the utility function of a human operator. In this approach, a set of final decisions of the operator is observed. For instance, the final treatment plans that a clinician chose for a patient or the dietary choices that a patient made to control their disease while also considering her own personal preferences. Based on these observations, we develop a new framework that uses inverse optimization to infer how the operator prioritized different trade-offs to arrive at her decision. 

We develop a new inverse optimization framework to infer the constraint parameters of a linear (forward) optimization based on multiple observations of the system. The goal is to find a feasible region for the forward problem such that all given observations become feasible and the preferred observations become optimal. We explore the theoretical properties of the model and develop computationally efficient equivalent models. We consider an array of functions to capture various desirable properties of the inferred feasible region. We apply our method to radiation therapy treatment planning—a complex optimization problem in itself—to understand the clinical guidelines that in practice are used by oncologists. These guidelines (constraints) will standardize the practice, increase planning efficiency and automation, and make high-quality personalized treatment plans for cancer patients possible.