Browsing by Author "Thorsen, Andreas H."

Now showing 1 - 4 of 4

Efficient frontiers in a frontier state: Viability of mobile dentistry services in rural areas
(2018-08) Thorsen, Andreas H.; McGarvey, Ronald G.
This study investigates the implications of adding mobile dentistry services to a community health center (CHC) in a rural area. CHCs are not-for-profit healthcare organizations which provide comprehensive primary care services to patients in the US, primarily for under-served and uninsured populations. We estimate the demand for the service in a five-county region in southwestern Montana, USA and work with stakeholders to determine a set of potential service locations. A mixed-integer optimization model is formulated to determine the frequency of stops in each location over a finite (six month) planning horizon with the goal of improving accessibility and availability of dental services while maintaining financial sustainability of the CHC. The financial considerations and social impact of offering a mobile dentistry service in southwestern Montana are assessed. Computational results based on a case study demonstrate the challenges facing mobile dentistry operations to increase access to under-served populations in a financially viable manner. Hybrid solutions, in which care is offered at a mix of fixed locations and mobile locations, appear to best balance the objectives of financial sustainability and expanded access to care.
Operational efficiency, patient composition and regional context of U.S. health centers: Associations with access to early prenatal care and low birth weight
(2019-04) Thorsen, Maggie L.; Thorsen, Andreas H.; McGarvey, Ronald G.
Community health centers (CHCs) provide comprehensive medical services to medically under-served Americans, helping to reduce health disparities. This study aimed to identify the unique compositions and contexts of CHCs to better understand variation in access to early prenatal care and rates of low birth weights (LBW). Data include CHC-level data from the Uniform Data System, and regional-level data from the US Census American Community Survey and Behavioral Risk Factor Surveillance System. First, latent class analysis was conducted to identify unobserved subgroups of CHCs. Second, data envelopment analysis was performed to evaluate the operational efficiency of CHCs. Third, we used generalized linear models to examine the associations between the CHC subgroups, efficiency, and perinatal outcomes. Seven classes of CHCs were identified, including two rural classes, one suburban, one with large centers serving poor minorities in low poverty areas, and three urban classes. Many of these classes were characterized by the racial compositions of their patients. Findings indicate that CHCs serving white patients in rural areas have greater access to early prenatal care. Health centers with greater efficiency have lower rates of LBW, as do those who serve largely white patient populations in rural areas. CHCs serving poor racial minorities living in low-poverty areas had particularly low levels of access to early prenatal care and high rates of LBW. Findings highlight that significant diversity exists in the sociodemographic composition and regional context of US health centers, in ways that are associated with their operations, delivery of care, and health outcomes. Results from this study highlight that while the provision of early prenatal care and the efficiency with which a health center operates may improve the health of the women served by CHCs and their babies, the underlying social and economic conditions facing patients ultimately have a larger association with their health.
Robust inventory control under demand and lead time uncertainty
(2015-12) Thorsen, Andreas H.; Yao, Tao
In this paper a general methodology is proposed based on robust optimization for an inventory control problem subject to uncertain demands and uncertain lead times. Several lead time uncertainty sets are proposed based on the budget uncertainty set, and a set based on the central limit theorem. Robust optimization models are developed for a periodic review, finite horizon inventory control problem subject to uncertain demands and uncertain lead times. We develop an approach based on Benders’ decomposition to compute optimal robust (i.e., best worst-case) policy parameters. The proposed approach does not assume distributional knowledge, makes no assumption regarding order crossovers, and is tractable in a practical sense. Comparing the new approach to an epigraph reformulation method, we demonstrate that the epigraph reformulation approach is overly conservative even when costs are stationary. The approach is benchmarked against the sample average approximation (SAA) method. Computational results indicate that the approach provides more stable and robust solutions compared to SAA in terms of standard deviation and worst-case solution, especially when the realized distribution is different than the sampled distribution.
Robust Optimization Model for a Dynamic Network Design Problem Under Demand Uncertainty
(2010-09) Chung, Byung Do; Yao, Tao; Xie, Chi; Thorsen, Andreas H.
This paper describes a robust optimization approach for a network design problem explicitly incorporating traffic dynamics and demand uncertainty. In particular, we consider a cell transmission model based network design problem of the linear programming type and use box uncertainty sets to characterize the demand uncertainty. The major contribution of this paper is to formulate such a robust network design problem as a tractable linear programming model and demonstrate the model robustness by comparing its solution performance with the nominal solution from the corresponding deterministic model. The results of the numerical experiments justify the modeling advantage of the robust optimization approach and provide useful managerial insights for enacting capacity expansion policies under demand uncertainty.