Efficient utilization of existing resources is crucial for cost containment in medical institutions. Accurately predicting surgery duration will improve the utilization of indispensable surgical resources such as surgeons, nurses, and operating rooms. Prior research has identified the Current Procedural Terminology (CPT) codes as the most important factor when predicting surgical case durations. However, there have been few attempts to create a general predictive methodology that can effectively extract information from multiple CPT codes. This research proposes two regression-based predictive models: (a) linear regression, and (b) log-linear regression models. To perform these regression analysis, a full-ranked design matrix based on CPT code inclusions in the surgical cases needs to be constructed. However, a naively constructed design matrix is ill conditioned (i.e., singular). A systematic procedure is proposed to construct a full-ranked design matrix by sifting out the CPT codes without any predictive power while retaining useful information as much as possible. The proposed models can be applied in general situations where a surgery can have any number of CPT codes and any combination of CPT codes. Using real-world surgical data, the proposed models are compared with benchmark methods and significant reductions in prediction errors are shown.

COVID-19 has exposed the global supply chains to great vulnerability. In such extreme circumstances, product availability becomes a primary concern. This paper studies a basic inventory management strategy—lateral transshipment—under decentralized systems, which may play an important role in dealing with stockouts during unexpected crises. Lateral transshipments not only react quickly to stockout, but are also environmentally friendly due to the significant reduction of production and transportation pollution. This paper studies optimal lateral transshipment and replenishment decisions under a decentralized setting. We construct a multi-stage stochastic model that captures demand uncertainty and customer switching behavior. We demonstrate that, similar to the centralized setting, the optimal transshipment decision follows a double-threshold structure. The optimal replenishment quantities are determined under two pricing mechanisms—individual mechanism (IP) and negotiated mechanism (NP). Numerical examples are provided to demonstrate the impact of lateral transshipment on supply chain cost reduction.

Brazos Valley Food Bank (BVFB) is a non-profit organization in the Bryan-College Station area of Texas. It distributes food supplies through partner agencies and special programs to eradicate hunger in Brazos Valley. However, a big gap exists between the meals distributed by BVFB and the size of the food-insecure population. This research is motivated by BVFB’s desire to reach more people by recruiting more sustainable partner agencies. We used Geographic Information Systems (GIS) to map food desert areas lacking access to nutritious food. We combined expert knowledge with multi-criteria decision-making (MCDM) to address the challenges and time consumption of manually identifying sustainable partner agencies for local food delivery. We identified evaluation criteria for all agencies based on BVFB managers’ preferences using a qualitative approach, and then applied three quantitative decision-making models: the Weighted Sum Model (WSM), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the Multi-criteria Optimization and Compromise Solution (VIKOR) models to obtain ranking results. We compared the quantitative models’ rankings to BVFB managers’ manual choices and discussed the impacts of our research. The key innovation of the research is to develop a mixed method by combining expert knowledge with mathematical decision models and GIS to support spatial decision making in food distribution. Although our results were specific to BVFB, these procedures can be applied to food banks in general. Future studies include finetuning our models to measure and address human biases, wider applications and more data collections.

It has been accepted that industry agglomeration has a significant impact on city-level productivity. However, very few researches have investigated the impact of industry agglomeration from the perspective of spatial distribution of firms and mutual effects between firms. This paper uses a network-based, within-city approach to shed new light on the impact of industry agglomeration. Based on a data set that includes records of all Chinese industrial firms from 1998 to 2007, we construct a network of firms for each prefectural level city and characterize within-city industry agglomeration using principal components obtained from network-based measurements and dimension reduction. Those characterizations allow us to examine the impact of industry agglomeration on total factor productivity through panel data regression. We obtain three principal components: modularity, scale, and diversity to characterize city-level industry agglomeration. Panel data regression reveals that all three principal components are significantly, positively related to a city’s total factor productivity. However, as the scale of firms in a city increases, the impact of agglomeration decreases. Meanwhile, relative to the impact of modularity and scale, the impact of diversity on TFP is weaker.