In an era defined by rapid technological advancements and increasingly complex global challenges, the ability to make smart, informed decisions is more crucial than ever. The 2024 volume assembles 12 outstanding chapters that bring together a collection of cutting-edge research and practical insights from a range of fields. The chapters explore how advanced methodologies and technologies — such as large language models, machine learning, data science, and mathematical optimization — are being leveraged to enhance efficiency, effectiveness, and strategic decision-making across domains, ranging from retail and humanitarian operations to healthcare.

The 2023 volume assembles eleven exciting chapters, presenting models and methodologies that advance the frontiers of OR/MS in the era of big data and emerging technologies. The chapters span methodological advances in deterministic optimization, simulation optimization, game theory, and machine learning; new developments, including digital twins and Python inventory optimization; and application areas in healthcare systems, supply chains, and humanitarian and military logistics.

This volume explores emerging and impactful topics in operations, including integration of prediction, behavior, information, and quantum information science with optimization and OR/MS models addressing operations resiliency. Applications include sports tournaments, pharmaceutical supply chain resilience, energy equity, financial inclusion, product recalls, COVID-19 pandemic, and teaching supply chain management with games and analytics.

This volume covers a range of exciting topics, including multi-objective optimization, sports analytics, machine learning, game theory, and discrete choice modelling. The chapters focus on many compelling applications for this analysis, including power systems, cancer radiotherapy, and the COVID-19 pandemic.

The chapters in the 2020 volume offer a contemporary look at meaningful impacts from operations research and operations management, including their impact upon society, practice, and theory. Applications areas include diversity, equity, and inclusion, algorithmic bias, healthcare, public education, military operations, wine analytics, financial clearing networks, economics of information, and innovation.

The chapters in this volume highlight the meaningful impacts from operations research (OR) and operations management (OM) on society, practice, and theory. The chapters highlight the increasing focus on fairness, the need for multidiscipliinarity, and the importance of practice. The reader will find the volume to span a number of important and interesting applications of OR/OM/anaytics techniques, highlighting where and how our community can contribute to pressing and impactful topics around the globe today.

The chapters in this year’s volume span a number of exciting topics in modeling, optimization, and decision making for contemporary OR/MS problems. The areas covered include machine learning, chronic disease modeling, food safety, financial networks, and production problems; methodological advances are presented for deterministic and stochastic optimization, as well as several heuristic approaches. Expert advice from practitioners involved in real-world applications is also provided.

The chapters are written by a diverse array of experts working across a variety of institutes. Their research covers a range of exciting topics, including OR/MS frontiers in mixed-integer programming, stochastic programming, and Markov decision processes; optimization and deep learning; O.R. approaches for biology, energy and health care systems; and other emerging developments from traditional INFORMS fields.

This volume covers a range of exciting topics, including multiobjective optimization, optimization under uncertainty, big data analytics, project management, and risk modeling and optimization. The chapters focus on many compelling applications for which this analysis is useful, including those arising in finance, healthcare, and energy systems.

The chapters in this volume discuss research in data-driven stochastic programming, multi-stage robust optimization, meta-algorithms, uncertainty modeling in power systems, data analytics in neuroimaging, ranking and selection methods, and discrete optimization for homeland security and disaster management.

This volume focuses on decision-making as a system of data analysis, optimization theory, computing technology, and policy, and highlights the theme of the 2014 INFORMS Annual Meeting, Bridging Data and Decisions.