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Throughput values for systems with N = 2 and non-homogeneous tasks 4 Throughput values for systems with N = 3 and non-homogeneous tasks 5 Throughput values for systems with N = 4 and non-homogeneous tasks 6 Throughput values for systems with N = 5 and non-homogeneous tasks 7 Throughput values for systems with N = 2 and homogeneous tasks . 8 Throughput values for systems with N = 3 and homogeneous tasks . 9 Throughput values for systems with N = 4 and homogeneous tasks . 10 Throughput values for systems with N = 5 and homogeneous tasks . 11 Throughput values for systems with N = 3, 4, 5 and unequal buffer sizes 12 Throughput values for systems with N = 3 and equal buffer sizes . . 13 Throughput values for systems with N = 4 and equal buffer sizes . . 14 Throughput values for systems with N = 5 and equal buffer sizes . .

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Robust parameter design (RPD) aims to build product quality in the early design phase of product development by optimizing operating conditions of process parameters. A vast majority of the current RPD studies are based on an uncensored random sample from a process distribution. In reality, censoring schemes are widely implemented in lifetime testing, survival analysis, and reliability studies in which the value of a measurement is only partially known.However, there has been little work on the development of RPD when censored data are under study. To fill in the research gaps given practical needs, this paper proposes response surface-based RPD models that focus on survival times and hazard rate. Primary tools used in this paper include the Kaplan-Meier estimator, Greenwood's formula, the Cox proportional hazards regression method, and a nonlinear programming method. The experimental modeling and optimization procedures are demonstrated through a numerical example. Various response surface-based RPD optimization models are proposed, and their RPD solutions are compared.

Since the World Health Organization declared the novel coronavirus disease a pandemic, more than 2 million cases of infections and 140,000 deaths have been reported across the world. Specialty physicians are now working as frontline workers due to hospital overcrowding and a lack of providers, and this places them as a high-risk target of the epidemic. Within these specialties, anesthesiologists are one of the most vulnerable groups as they come in close contact with the patient's airway. An agent-based simulation model was developed to test various staffing policies within the anesthesiology department of the largest healthcare provider in Upstate South Carolina. We demonstrate the benefits of a restricted, no mixing shift policy, which segregates the anesthesiologists as groups and assigns them to a shift within a single hospital. Results consistently show a reduction in the number of deaths, anesthesiologists not available to work, and the number of infected anesthesiologists. INTRODUCTIONApproximately 2.9 million cases of infections and 200,000 deaths have been recorded around the world under the current outbreak of severe respiratory syndrome coronavirus 2 (SARS-CoV-2) (John Hopkins University and Medicine 2020). Of these, more than one-third of the infections and one-fourth of the deaths are recorded in the US, placing the country in a unique, unprecedented public health emergency situation (Centers for Disease Control and Prevention 2020a). The rapid proliferation of the virus and surge in cases has overwhelmed the health systems, first responders, and providers. Additionally, lack of resources, including hospital beds, testing kits, ventilators, and providers, has created a significant delay in providing patient care and has resulted in significant hospital overcrowding. This additional patient demand on healthcare facilities has various negative impacts, one being the increased risk of infection transmission within the healthcare facility (Ng et al. 2020). Given that a majority of the infected people act as asymptomatic carriers of the virus, the impact on the healthcare providers and on other patients is exacerbated (World Health Organization 2020a; Day 2020). A February 2020 case study observed that one patient potentially exposed 41 healthcare workers to the SARS-CoV-2 virus, forcing the hospital to quarantine these providers (Ng et al. 2020).Although various effective actions, including increasing the production of ventilators, testing kits, and setting up temporary hospital facilities, have been adopted to thwart the damage, one of the critical issues faced is the physician shortage (MedPage Today 2020). Currently, physicians from different specialties, retired physicians, and early-graduated medical students are all contributing to the workforce (Time 2020).