Editorial Statement—Optimization and Decision Analytics

Optimization and Decision Analytics

Peng Sun, Duke University

Teo Chung Piaw, National University of Singapore

Optimization models and methods have enabled many business innovations in today’s economy. These advancements improve the scale, speed, and quality of decision-making processes, which are essential for organizational success and societal benefit. The Opti-mization and Decision Analytics Department focuses on the publication of research that introduces innovative optimization techniques and applications or advances methodologies for prescriptive decision making to provide decision makers with actionable insights by applying mathematical models to analyze complex scenarios and optimize outcomes.

We invite contributions covering a broad range of models and methods for optimal decision making, be they static or dynamic, deterministic or stochastic in nature, supporting a single decision maker or managing strategic interactions of multiple players. We encourage studies that propose new approaches in interdisciplinary contexts and demonstrate a clear understanding of the complexities and nuances of modern business environments. One such example is the confluence of theoretical optimization methodologies and cutting-edge practical data analytics. Papers that showcase innovative methodologies, offer insightful analyses, and present scalable solutions to contemporary problems that arise from businesses and public sectors are particularly welcome.

Compared with other leading journals on optimization and decision analytics, Management Science emphasizes research with practical impacts. Papers leaving a lasting impact tend to offer simple and robust solutions and contain sharp and insightful messages. Therefore, although we appreciate deep analysis and rigorous validation, we also look for clear applicable insights that resonate with a diverse audience.