Department of Information Technology and Operations Management

This empirical study examines decision-making in project selection in the face of overwhelming flood infrastructure needs and inadequate resources, particularly in vulnerable communities. The motivation for this study is to explore the interconnectedness between socioeconomic dimensions and environmental risks in the decision-making process for selecting projects. The study evaluates the Palm Beach County project selection framework and the impact of multi-criteria decision-making on project selection by proposing a new framework. The new project selection framework emphasizes the integration of flood risk and social vulnerability index criteria to evaluate the relationship between the new criteria in the decision-making framework and project selection.
The analysis is comprised of 24 models grouped into three distinct groups and compared using paired t-tests. The analysis reveals that of the three groups, the group which incorporates both flood risks and social vulnerability criteria consistently outperforms the others, demonstrating its effectiveness in providing a more equitable investment for vulnerable communities that are more susceptible to floods. The findings provide valuable insights and recommendations for practitioners and scholars, emphasizing the need for a theoretical framework with objectivity to guide optimal infrastructure investments for decision makers.

Supply chain challenges have been significantly affected by both demand and supply on a global level. The selection of manufacturing countries has become critical to firms and their boards, even more so coming out of the COVID-19 global pandemic. The present study focuses on how firms select countries and regions to de-risk future global apparel sourcing, as countries that have been dependable in the past may not be in the future based on frequent environmental jolts, legacy supply chain failures, shifting government policy, and extreme volatility. The result of this study is a decision model for manufacturing country selection. This research was focused on the apparel industry; however, further research may indicate that it is applicable to other industries. A group of criteria was selected, the relative significance of these criterion was determined using the Analytical Hierarchy Process (AHP). The AHP methodology was applied in a case study as a decision-making tool to enable decision-makers to assess the most suitable countries for manufacturing country selection. The result of this study is a decision model for manufacturing country selection based on multiple criteria weighted by industry experts using Analytical Hierarchy Process (AHP). In developing the model we utilize data from 61 countries representing over 95% of all the global apparel exports, with criteria utilized originating from 10 indices.

The world is ever-changing with technological advancement. National economies and private organizations are shifting their infrastructure to adapt to innovation and technology. We are seeing a major shift in our transportation ecosystem as well. Automotive manufacturers are launching fully electric semi-truck (EST) on the road for freight transportation. Electric trucks will have a long-term effect on many industries and the national economy in the United States. Compared to conventional automobiles, the limited range of electric vehicles is a major obstacle. To adapt electric vehicles (EVs) to our transportation system, the U.S. needs a proper charging infrastructure in our grid. Though we have been adapting the passenger EVs, the EST needs larger charging infrastructure capabilities to charge the large batteries of these trucks to complete the journey. The most important aspect is the geographical locations of these mega charging stations along U.S. highways. To analyze the optimal locations of these charging infrastructures, we use the framework from Csiszár et al. (2020), an origin-destination (O-D) data model. OD is classified as the original location of the freight to the end destination. We also use the flow-refueling location model (FRLM) from He et al. (2019). This framework showcases the optimal locations in each route in order to complete the OD pairs. We use data from the U.S. department of energy for the locations of charging stations. Furthermore, we use U.S. department of transportation highway & transportation data to procure the major O-Ds of freight transportation.

While the complexities and challenges facing healthcare continue to grow, the focus on improving surgical practices remains constant. Possessing a strong influence over patient referral patterns, public reputation/prominence, and financial performance, surgical practices command heightened attention on operational performance and clinical outcomes. Executive leadership cannot support (nor improve) a surgical practice without comprehending the importance of team dynamics in the operating room (OR) environment.
Previous literature offers mixed and incomplete results on themes of team familiarity and OR efficiency, frequently citing handoffs, late starts, and task disruptions as catalysts for negative performance. Studies routinely use historical interaction counts to measure team familiarity, which often neglect the degree of participation (engagement) across prior experiences. Similarly, counts of handoffs or individuals entering an OR do not offer an accurate assessment of team performance. Guided by historical studies, four hypotheses are presented and argue that enhancing surgical team dynamics yield favorable improvements for operational performance and clinical outcomes. Utilizing data from 9,049 neurologic surgery cases performed at two separate campuses (belonging to the same organization) over a three-year timeframe (March 2019 to November 2021), this study measures surgical team dynamics in a highly complex setting through the lens of case continuity and surgeon familiarity to assess key outputs: case scheduling errors (proxy for operational performance) and post-operative complications within 30-days of surgery (proxy for clinical outcomes).

Florida Atlantic University Departmental Dashboard Indicators. Department program reviews for College of Business, Florida Atlantic University.

Florida Atlantic University Departmental Dashboard Indicators. Department program reviews for College of Business, Florida Atlantic University.

Florida Atlantic University Departmental Dashboard Indicators. Department program reviews for College of Business, Florida Atlantic University.

Florida Atlantic University Departmental Dashboard Indicators. Department program reviews for College of Business, Florida Atlantic University.

Florida Atlantic University Departmental Dashboard Indicators. Department program reviews for College of Business, Florida Atlantic University.

Florida Atlantic University Departmental Dashboard Indicators. Department program reviews for College of Business, Florida Atlantic University.