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One commonly used workload metric in Emergency Medical Services (EMS) is the Unit Hour Utilization (UHU). The UHU is a productivity measure that, by definition, represents the ratio of patient transport calls to the total hours that ambulances are staffed. It is often misinterpreted as a utilization measure representing the percentage of crews’ available working hours that are spent performing work. This paper investigates a surrogate model to estimate a measure of EMS crew utilization that considers not only call response, but also indirect work tasks, such as documentation and shift start activities. We explored Kriging, KPLS, RBF, and physics-based models based on EMS work dynamics. The true measure of utilization was based on Montecarlo samples of estimated work time patterns associated with a year’s worth of dispatch data augmented with the results of a work measurement study. The best performing model in terms of the root mean square error (RSME), the symmetric mean absolute percent error (sMAPE), and Pearson correlation estimates, was the physics-based model. This model requires time studies to estimate the average time spent in shift start activities and documenting calls, geographic information systems to estimate the average time driving back to the post, and dispatch data analysis to estimate the average time to respond to calls. Sensitivity analysis was used to provide recommendations for when to update these parameters and general recommendations were given to implement this approach in other EMS systems. 

This research investigates optimal pricing strategies in a service-providing queueing system where customers may renege before service completion. Prices are quoted upon customer arrivals and the incoming customers join the system if their willingness to pay exceeds the quoted price. While waiting in line or during service, customers may get impatient and leave without service, incurring an abandonment cost. There is also a per-unit time per-customer holding cost. Our objective is to maximize the long-run average profit through optimal pricing policies. We model the problem as a Markov decision process and identify the optimal pricing using policy iteration. We also study the structure of the optimal pricing policy. Furthermore, we show that under mild assumptions, the optimal price increases as the number of customers in the system increases. When those assumptions do not hold, optimal price decreases and then increases as the number of customers in the system grows. 

This research investigates optimal pricing strategies in a service-providing queueing system where customers may renege before service completion. Prices are quoted upon customer arrivals and the incoming customers join the system if their willingness to pay exceeds the quoted price. While waiting in line or during service, customers may get impatient and leave without service, incurring an abandonment cost. There is also a per-unit time per-customer holding cost. Our objective is to maximize the long-run average profit through optimal pricing policies. We model the problem as a Markov decision process and identify the optimal pricing using policy iteration. We also study the structure of the optimal pricing policy. Furthermore, we show that under mild assumptions, the optimal price increases as the number of customers in the system increases. When those assumptions do not hold, optimal price decreases and then increases as the number of customers in the system grows. 

Innovation competitions and programs (ICPs) are becoming pivotal in STEM education by immersing students in experiential learning that fosters an innovation mindset. However, measuring the transformative impact of ICPs on students remains a challenge due to the lack of validated frameworks. This study introduces the Transformative Learning Scale for the Innovation Mindset (TLSIM), designed to assess shifts in self-awareness, open-mindedness, and innovation capabilities among STEM students. Using Transformative Learning Theory (TLT) and Kern Entrepreneurial Engineering Network's (KEEN) 3Cs framework (Curiosity, Connections, Creating Value), TLSIM was developed and validated through expert reviews, focus groups, and psychometric analysis with data from 291 STEM students, 70.2% of whom were engineering majors. Confirmatory Factor Analysis revealed strong psychometric properties, demonstrating TLSIM’s reliability and validity in capturing transformative learning in ICP contexts. The scale emphasizes the multidimensional nature of the innovation mindset and focuses on both the outcomes and processes of developing an innovation mindset. Thereby, the TLSIM provides a new way for educators and program designers to assess and enhance the effectiveness of ICPs in fostering innovation mindsets in STEM education. The paper also presents preliminary findings about the connections between ICP processes and innovation mindset. The TLSIM tool has the potential to significantly improve the assessment and development of ICPs, which could result in more impactful innovations in STEM education. Future research will focus on refining the instrument and exploring its cross-disciplinary applications.