Optimal Fleet Size of an Integrated Production and Distribution Scheduling Problem for a Single Perishable Product

This dissertation focuses on a practical production problem in which a perishable product must be produced and distributed at minimum cost. The problem has some features of the integrated production and distribution scheduling problem in that we seek to determine the fleet size and their routes subject to a planning horizon constraint but there are significant differences as well. In particular, this research differs because the product has a limited lifetime, the total demand must be satisfied within a planning horizon, multiple trucks can be used, and the production schedule and the distribution sequence are considered. Two mixed integer programming models are formulated to solve the single plant and two-plant problems and, then, heuristics based on evolutionary algorithms are provided to resolve the models in a reasonable tim

Predicting Patients at Risk for 3-Day Postdischarge Readmissions, ED Visits, and Deaths

Background: Transitional care interventions can be utilized to reduce post-hospital discharge adverse events (AEs). However, no methodology exists to effectively identify high-risk patients of any disease across multiple hospital sites and patient populations for short-term postdischarge AEs. Objectives: To develop and validate a 3-day (72 h) AEs prediction model using electronic health records data available at the time of an indexed discharge. Research Design: Retrospective cohort study of admissions between June 2012 and June 2014. Subjects: All adult inpatient admissions (excluding in-hospital deaths) from a large multicenter hospital system. Measures: All-cause 3-day unplanned readmissions, emergency department (ED) visits, and deaths (REDD). The REDD model was developed using clinical, administrative, and socioeconomic data, with data preprocessing steps and stacked classification. Patients were divided randomly into training (66.7%), and testing (33.3%) cohorts to avoid overfitting. Results: The derivation cohort comprised of 64,252 admissions, of which 2782 (4.3%) admissions resulted in 3-day AEs and 13,372 (20.8%) in 30-day AEs. The c-statistic (also known as area under the receiver operating characteristic curve) of 3-day REDD model was 0.671 and 0.664 for the derivation and validation cohort, respectively. The c-statistic of 30-day REDD model was 0.713 and 0.711 for the derivation and validation cohort, respectively. Conclusions: The 3-day REDD model predicts high-risk patients with fair discriminative power. The discriminative power of the 30-day REDD model is also better than the previously reported models under similar settings. The 3-day REDD model has been implemented and is being used to identify patients at risk for AEs