50th Anniversary Perspective and Editorial Statement

50th Anniversary Perspective

As Mathematics of Operations Research marks its 50th anniversary, this editorial statement reaffirms the journal’s mission while recognizing the continued evolution of the field. Over five decades, Mathematics of Operations Research has expanded its scope by engaging deeply with neighboring areas such as complexity theory, algorithms, stochastic modeling, and learning, while maintaining a consistent emphasis on mathematical depth, originality, and enduring significance.

The present revision reflects this tradition, acknowledging the accelerating pace of methodological and technological change while underscoring Mathematics of Operations Research’s role in advancing the mathematical foundations that support decision making, operations research, and management science. In doing so, the journal remains firmly rooted in its history while continuing to engage with emerging directions shaping the future of the field.

Editorial Statement

Mathematics of Operations Research is a premier methodological journal of the Institute for Operations Research and the Management Sciences (INFORMS). The journal invites articles on the mathematical and computational foundations of operations research and management science, including continuous, discrete, and stochastic optimization; stochastic processes and models; game theory; learning, computation, and adaptive decision making; and related methodological areas relevant to OR/MS.

The journal also welcomes contributions at the interface with neighboring methodological areas and in emerging directions in decision making and computation, including probability, statistics, artificial intelligence, learning, automated reasoning, and quantum computation, provided that these works advance foundational understanding relevant to operations research and management science.

As the field has evolved, the boundaries among methodological areas have become increasingly permeable. Mathematics of Operations Research welcomes contributions that span multiple areas. When a paper engages several areas, authors should choose the one that best matches the paper’s central methodological contribution and the community for which its insights are most relevant. Manuscripts may be reassigned by the editors if another area provides a more natural fit.

The emphasis is on originality, quality, and importance; mathematical correctness alone is not sufficient. While the journal emphasizes mathematical methodology, authors are expected to clearly position their contributions in the context of mainstream operations research and decision-making problems, and to provide motivation that makes this relevance transparent. Significant developments in operations research and management science that do not have substantial mathematical depth are more appropriately directed to other journals in the INFORMS family.

Area Editorial Statements

Each area statement reflects the journal’s common standards of originality, significance, and mathematical depth, while allowing for differences in emphasis across methodological communities.

The journal’s editorial areas are intended to provide a durable structure, but not a rigid one. As the field evolves, Mathematics of Operations Research will continue to refine the scope of its areas and engage with emerging methodological directions that may shape the future of OR/MS.

• Continuous Optimization
• Discrete Optimization
• Game Theory
• Learning & Computation
• Stochastic Models

Continuous Optimization

The Continuous Optimization area of Mathematics of Operations Research focuses on the development and analysis of mathematical models, theory, and algorithms for optimization problems involving continuous decision variables. We welcome submissions that advance the methodological and theoretical foundations of continuous optimization and contribute broadly to operations research and mathematical optimization.

Continuous optimization is a central component of modern decision making and underpins many areas of operations research, as well as related disciplines such as machine learning, statistics, economics, and engineering. Increasing problem scale, uncertainty in data and models, and the need for real-time decision making continue to drive advances in optimization theory and algorithms.

Problem classes include, but are not limited to, convex and conic optimization, including semidefinite programming; nonconvex and nonsmooth optimization; variational inequalities; and stochastic, robust, or dynamic optimization. Methodological topics include first-order, higher-order, and zeroth-order (derivative-free) methods; stochastic and randomized algorithms; algorithms for large-scale and structured problems; convergence analysis and oracle complexity; as well as duality, convex analysis, and variational analysis.

Discrete Optimization

Discrete optimization is the study of optimization problems with an inherent discreteness or combinatorial aspect in their decision spaces. It has applications in diverse areas ranging from supply chain management and manufacturing to healthcare and energy.

Its mathematical foundations bring together ideas from discrete mathematics and combinatorics, convex analysis and geometry, polyhedral theory, geometry of numbers, probability theory, and computational complexity theory.

We invite papers with significant contributions to discrete optimization, including but not limited to discrete and convex geometry, polyhedral combinatorics, deterministic and stochastic combinatorial optimization, mixed-integer optimization, approximation and online algorithms, learning-augmented algorithm design, and quantum computational aspects of discrete optimization.

Game Theory

The Game Theory area of Mathematics of Operations Research welcomes papers that develop the mathematical aspects and foundations of game theory in ways that are motivated by problems arising in operations research and management science.

Topics include stochastic, repeated, differential, and mean-field games; equilibrium concepts and refinements; evolutionary game theory; learning in games; and algorithmic game theory, auctions, market and mechanism design, matching, and social choice.

Submissions should clearly articulate their insights, motivation, and significance for a broad OR/MS audience. Highly technical papers lacking clear motivation or OR/MS context are unlikely to fit this area.

Learning & Computation

The Learning & Computation area welcomes mathematically grounded contributions at the interface of operations research with learning, artificial intelligence, and emerging computational paradigms.

Topics include learning-augmented algorithms, online learning, bandits, reinforcement learning, automated reasoning, theorem proving, formal verification, quantum algorithms, and other computational approaches that contribute foundational insight relevant to OR/MS.

Papers focused primarily on implementation or empirical performance, without substantial methodological or theoretical contribution, are generally not a good fit for this area.

Stochastic Models

The Stochastic Models area seeks to publish high-quality research on applied probability, stochastic analysis, stochastic computation, stochastic control, stochastic games, and stochastic optimization relevant to operations research.

Applications span queueing, finance, manufacturing, supply chains, communication networks, healthcare, energy, transportation, social networks, and emerging data-driven domains.

Submissions are evaluated based on novelty, insight, breadth of applicability, and potential to open new research directions. Motivation and implications are as important as technical correctness.