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Application of Artificial Intelligence/Machine Learning to Operations Research

Mckenzie, Taylor K.; Abel, Kelsey C.; Flory, John A.; Kelic, Andjelka; Orr, Marilee K.; Bays, Nathan R.

This report examines the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on operations research, private industry, and government sectors, highlighting their applications in automating processes, enhancing decision-making, and optimizing complex systems. AI/ML technologies have revolutionized industries through predictive maintenance, supply chain optimization, and autonomous systems, while also advancing public safety and defense operations. However, challenges such as data integrity, model transparency, and the need for human oversight persist, particularly in high-consequence environments. The report emphasizes the critical role of explainable AI (XAI) and human-computer interaction models like Human-in-the-Loop (HITL) and Human-on-the-Loop (HOTL) in fostering trust and accountability. Balancing automation with ethical responsibility and transparency is essential for the continued successful integration of AI/ML into operational and strategic decision-making frameworks.

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2021-2022 Remote Work Study Final Results

Hammer, Ann E.; Abel, Kelsey C.; Joiner, Alexis T.

The COVID-19 pandemic has forced many organizations—from national laboratories to private companies—to change their workforce model to incorporate remote work. This study and the summarized results sought to understand the experiences of remote workers and the ways that remote work can impact recruitment and retention, employee engagement, and career development. Sandia, like many companies, has committed to establishing a hybrid work model that will persist postpandemic, and more Sandia employees than ever before have initiated remote work agreements. This parallels the nationwide increase in remote employment and motivates this study on remote work as an enduring part of workforce models.

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8 Results
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