W e model the global vehicle supply chain of an International Humanitarian Organization (IHO) with a dynamic hub location model across monthly periods. We use actual vehicle data from the International Federation of the Red Cross to feed our model and provide insights into IHO secondary support demand. We find that secondary support demand for items such as vehicles is different from primary beneficiary demand for items such as water and food. When considering disaster response and development program demand simultaneously (disaster cycle management), our results illustrate that keeping a lean centralized hub configuration with an option for temporary hubs in mega disaster locations can reduce overall supply chain costs over a long time horizon. We also show that it is possible to structure a supply chain to take operational advantage of earmarked funding. This research lays the groundwork for using optimization models to analyze disaster cycle management.

Hospital readmissions present an increasingly important challenge for healthcare organizations. Readmissions are expensive and often unnecessary, putting patients at risk and costing $15 billion annually in the US alone. Currently, 17% of Medicare patients are readmitted to a hospital within 30 days of initial discharge with readmissions typically being more expensive than the original visit to the hospital. Recent legislation penalizes organizations with a high readmission rate. The medical literature conjectures that many readmissions can be avoided or mitigated by post-discharge monitoring. To develop a good monitoring plan it is critical to anticipate the timing of a potential readmission and to effectively monitor the patient for readmission causing conditions based on that knowledge. This research develops new methods to empirically generate an individualized estimate of the time to readmission density function and then uses this density to optimize a post-discharge monitoring schedule and staffing plan to support monitoring needs. Our approach integrates classical prediction models with machine learning and transfer learning to develop an empirical density that is personalized to each patient. We then transform an intractable monitoring plan optimization with stochastic discharges and health state evolution based on delay-time models into a weakly-coupled network flow model with tractable subproblems after applying a new pruning method that leverages the problem structure. Using this multi-methodologic approach on two large inpatient datasets, we show that optimal readmission prediction and monitoring plans can identify and mitigate 40%-70% of readmissions before they generate an emergency readmission.

Hunger occurs in all locations around the globe, from developing to developed countries. In fact, there were over 37 million food insecure individuals (those without access to consistent nutritious food) in the United States in 2018, and this number increased in recent years due to the COVID pandemic. In many countries, food banks are used to consolidate food donations from individuals or government agencies and then provide that food to local partner agencies (such as food pantries and soup kitchens), who distribute it to food insecure individuals. As nonprofit humanitarian organizations, food banks strive to achieve geographic equity in their food distribution, so one area (or county) is not favored over others. However, food banks also want to maximize food distribution with their limited budgets. This equitable distribution versus cost balancing act is made even more challenging since food banks experience extreme variability in both the supply (donations) of food and partner agencies' capacity to deliver food to the food insecure. Our paper focuses on how mobile pantry programs, additional food bank storage capacity, and improved partner agency capacity can be utilized to address this supply and distribution capacity variability while considering food expiration times. Mobile pantry programs allow food banks to distribute food directly to the food insecure by sending their own trucks and employees to locations where food is most needed. Although all three of these approaches can be helpful, our results show that mobile pantries are a more effective approach to achieve high equity levels. This is especially true in the case of produce with relatively short expiration times. We also find that utilizing mobile pantry programs can increase equitable partner agency distribution considerably, because even small amounts of mobile pantry distribution in under-served areas allow for more equitable partner agency distribution in areas with available partner agency distribution capacity. Our research is based on data from our partner food bank, but our modeling and extensive sensitivity analysis should be applicable to many food banks with a similar collection and distribution structure.