Editorial Statement

The theory and practice of computing and operations research are necessarily intertwined. The INFORMS Journal on Computing publishes high quality papers that expand the envelope of operations research and computing. We seek original research papers on relevant theories, methods, experiments, systems, and applications. We also welcome novel survey and tutorial papers, and papers describing new and useful software tools. We expect contributions that can be built upon by subsequent researchers or used by practitioners.

Areas and Area Editors

Editorial statements for each Area are listed to guide authors in the selection of an appropriate Area for submission.

• Artificial Intelligence & Optimization
• Computational Modeling & Applications
• Design & Analysis of Algorithms — Continuous
• Design & Analysis of Algorithms — Discrete
• Heuristic Search & Approximation Algorithms
• Network Optimization:  Algorithms & Applications
• Quantum Computing
• Simulation, Stochastic Models, & Stochastic Optimization
• Software Tools

Artificial Intelligence & Optimization

Willem-Jan van Hoeve
Carnegie Bosch Professor of Operations Research
Tepper School of Business
Carnegie Mellon University
Pittsburgh, Pennsylvania, United States
[email protected]

The Artificial Intelligence & Optimization area focuses on the development and computational study of methods that integrate modern artificial intelligence techniques, data-driven modeling, and optimization technology for algorithm design and decision-making. Topics of interest include but are not limited to:

• AI for Optimization: Use AI methods to improve optimization algorithms, solvers, and systems. Examples include machine learning for selecting branching rules or cutting planes, integrating trained neural networks into optimization models, and stand-alone neural combinatorial optimization solvers.
• Optimization for AI: Use optimization methods to improve AI algorithms and systems. Examples include integrating optimization knowledge into the machine learning pipeline for decision-focused learning, using integer optimization to find optimal decision trees or subset selection for regression, and designing neural networks of minimal size.
• Data-Driven Optimization and Intelligent Decision Systems: Advance the integration of machine learning and optimization to build and evaluate decision systems that are both predictive and prescriptive. Research in this area bridges modern ML methodologies, including deep learning, reinforcement learning, and large language models, with optimization paradigms such as stochastic programming, robust optimization, and integer programming. Topics of interest include, but are not limited to, end-to-end predict-then-optimize pipelines, learning objectives and constraints from data, ML-embedded optimization, online learning with operational constraints, and inverse optimization.

Computational Modeling & Applications

Jim Luedtke
College of Engineering
University of Wisconsin–Madison
Madison, Wisconsin, United States
[email protected]

Computational modeling and operations research (OR), including all aspects of optimization, stochastic processes, and simulation, are vital tools for investigating a wide variety of applications. In turn, the computational and modeling challenges associated with these applications have spawned new developments in operations research, creating a synergy of application, theory, and implementation.

We welcome submissions in the following related topics:

Computational Modeling: Research on ways of creating and managing OR models which feature a significant computational/computing component. This includes computational techniques to model uncertainty, represent complex data sets and solutions, and capture complex decisions, objectives, and constraints. It also covers techniques to acquire constraints and elucidate preferences, algebraic modeling languages, and graphical representations. Research on methods to analyze models and model instances to provide useful insights is also in scope. Examples include computational methods and tools for explaining, debugging, visualizing, and analyzing OR models.

Applied Computational OR and Interdisciplinary Decision Systems: Advance the application of computational OR methodology – whether alone or combined with machine learning, simulation, and artificial intelligence – to address complex, real-world decision problems. Submissions should demonstrate meaningful contributions across the application domain, OR/management science methodology, and computing, with reviewers drawn from multiple disciplines accordingly. Papers whose novelty lies primarily in the application domain or in methodology alone should be directed to more specialized venues. Domains of interest include, but are not limited to, healthcare, energy and sustainability, transportation and logistics, supply chain, public sector and humanitarian operations, finance, workforce management, cybersecurity, digital marketing, and sports analytics. New and emerging applications of computational OR – especially those involving large-scale data, real-time decision-making, or novel societal challenges – are encouraged.

IJOC has a history of publishing papers related to biological systems, medicine, and healthcare delivery and such applications are welcome in this area. Additional application areas of interest include, but are not limited to, power systems, transportation, supply chain and logistics systems, employee staffing and scheduling, public sector, finance, cybersecurity, sustainability, natural resources, disaster management, sports, defense, digital marketing, and hospitality management. New and emerging applications of computational OR tools are especially encouraged.

The scope of this area is limited to papers that use or create OR tools and have a significant computational/computing contribution. Also, submissions that are about a particular OR related software tool should be directed to the Software Tools area.

Design & Analysis of Algorithms — Continuous

Veronica Piccialli
Department of Computer, Control, and Management Engineering Antonio Ruberti (DIAG)
Sapienza Università di Roma
Rome, Italy
[email protected]

The Design & Analysis of Algorithms — Continuous area focuses on the design, theoretical analysis, and computational evaluation of algorithms for efficiently solving continuous optimization problems, including both convex and nonconvex formulations. The area is particularly interested in methodological contributions introducing novel algorithmic ideas supported by solid theoretical guarantees, convincing empirical performance, or acombination of both.

Topics of interest include, but are not limited to, convex and nonconvex optimization methods, first and second-order algorithms, decomposition techniques, proximal and splitting methods, stochastic and online optimization, large-scale optimization algorithms, and acceleration techniques. Contributions addressing constrained optimization, robust and stochastic optimization, bilevel optimization, as well as approaches that integrate ideas from machine learning or address emerging computational challenges, are also welcome.

The emphasis is on significant methodological innovations applicable to a wide range of problem classes. Rigorous theoretical analysis (e.g., convergence guarantees or complexity bounds) or comprehensive, well-designed computational experiments must convincingly demonstrate the effectiveness and generality of the proposed methodology.

Demonstrating improved performance over existing methods on selected benchmark instances is generally insufficient for publication unless the results represent a significant advance over state-of-the-art approaches across a sufficiently wide range of problems. When contributions are primarily computational in nature, particular emphasis should be placed on sound experimental design and the reproducibility of results.

Finally, the importance of the continuous optimization problem(s) studied must be demonstrated by practical relevance or by a substantial body of prior research in the literature.

Design & Analysis of Algorithms — Discrete

Simge Küçükyavuz
McCormick School of Engineering
Northwestern University
Evanston, Illinois, United States
[email protected]

The Design & Analysis of Algorithms — Discrete area seeks to publish significant contributions to the exact solution of discrete optimization problems. The development of exact algorithms is a vibrant field driven by the need to develop general-purpose solvers capable of tackling broad classes of problems (such as Mixed-Integer Linear and Nonlinear Programming) and to exploit problem-specific structures to push the boundaries of solvability.

We explicitly focus on exact methods that provide proofs of optimality. Topics of interest include, but are not limited to:

• Integer & Mixed-Integer Programming:Branch-and-bound, branch-and-cut, and branch-and-price algorithms.
• Polyhedral Combinatorics: Strong formulations, cutting plane generation, and lifting techniques.
• Decomposition Techniques: Benders decomposition, Dantzig-Wolfe decomposition, and Lagrangian relaxation embedded in exact frameworks.
• Constraint & Logic-Based Optimization: Exact algorithms integrating constraint programming (CP), satisfiability (SAT), or logic-based Benders decomposition with mathematical programming.
• Enumerative Methods: Advanced search strategies, symmetry breaking, and filtering techniques for combinatorial problems.

Solvers and General-Purpose Methods We especially value the generality of proposed algorithms and their applicability within general-purpose solvers. Contributions that propose new branching rules, presolving/preprocessing techniques, or primal heuristics embedded within an exact framework (to accelerate convergence) are highly encouraged.

Authors are encouraged to select this area if their work focuses on algorithmic rigor and exactness in a discrete setting. Please consider the following distinctions when choosing an area:

• Constraint Programming & AI: While Constraint Programming is historically rooted in AI, papers utilizing CP or logic-based methods primarily to solve discrete optimization problems should be submitted to this area (Design & Analysis of Algorithms — Discrete). However, if the contribution focuses on integrating CP with machine learning, data science techniques, or purely AI-driven paradigms, it is likely more appropriate for the AI & Optimization area.
• MINLP: Algorithms for Mixed-Integer Nonlinear Optimization are inherently at the intersection of the Discrete and Continuous areas. Such contributions are welcome and should be submitted to the area that, in the judgment of the authors, is the most fitting.
• Stochastic Integer Programming: Algorithms for stochastic linear and integer programming often intersect with the Simulation, Stochastic Models, & Stochastic Optimization area. As with MINLP, such contributions are welcome here, particularly if they focus on structural decomposition or exact methods (e.g., L-shaped method, integer L-shaped method). The contribution should go to the area that, in the judgment of the authors, is the most fitting.
• Exact vs. Heuristic: Research focused on metaheuristics, local search, or approximation algorithms with performance guarantees should generally be submitted to the Heuristic Search & Learning area, unless the method is an integral component of an exact approach (e.g., a primal heuristic used to fathom nodes in a branch-and-bound tree).
• Applications: A paper should be submitted to this area if the algorithm is designed for a general class of problems or for an application not covered by a more specific area (such as Network Optimization or Computational Modeling & Applications).

The analysis of algorithms may concern all nontrivial aspects related to their completeness, correctness, and efficiency. Theoretical analysis (e.g., complexity proofs) and/or comprehensive computational experiments must convincingly demonstrate the effectiveness of the proposed methodology. When contributions are primarily computational, particular emphasis must be placed on sound experimental design, comparison against state-of-the-art exact methods, and reproducibility.

Heuristic Search & Approximation Algorithms

Domenico Salvagnin
Department of Engineering
Università di Padova
Padova, Italy
[email protected]

This area focuses on the design, analysis, and computational evaluation of efficient and innovative methods for approximately solving relevant, difficult (combinatorial) optimization problems. In particular, it covers topics such as approximation algorithms with performance guarantee, (fully) polynomial time approximation schemes, parameterized algorithms, and heuristics. New ideas in (deterministic or randomized) rounding and dynamic programming, greedy and local search algorithms, constrained programming-based methods, or hybrid approaches combining existing heuristic methods, alone or in conjunction with techniques from other areas of operations research (like matheuristics and MIP heuristics) or computer science, are also of particular interest. The emphasis within the area is on papers presenting methodological innovations that can be applied to a wide range of problems or situations and include a proof of performance, either theoretical or empirical. A simple assertion that the innovations can be applied elsewhere does not meet the burden of proof required in good scientific practice: the authors must demonstrate generality in a convincing manner. Rarely, it may be clear that a method is broadly important even when it is tested on only one problem, but this would be very unusual.

For many outlets in our field, a necessary and sufficient condition for publication is to show better results than a set of competitors over some test instances. While this may be a reasonable standard in some settings, it is generally insufficient for INFORMS Journal on Computing, unless the results indicate a significant improvement over state-of-the-art methods on a sufficiently wide range of problems. Obtaining best-known results will be helpful in making the case for the paper, but it is not normally sufficient. In addition, when the contribution is mostly of computational nature, a special emphasis must be given to sound experimental setup and to reproducibility of results.

The problem(s) considered in the paper must be “important” in some sense, though this is difficult to define precisely and is subject to some tradeoffs. Importance may be demonstrated by application to a problem of practical significance, or demonstration that the problem has been extensively studied in the research literature, for example.

Network Optimization:  Algorithms & Applications

Russell W. Bent
Los Alamos National Laboratory
[email protected]

The Network Optimization Area focuses on networks across the social, natural, physical, and engineering sciences. This focus ranges from engineered networks like electric grids, telecommunications, and transportation, to network models of social interactions, and to natural processes like biological networks. Of primary interest are those papers that combine fundamental contributions in the advancement of computational methods for network science (optimization, game theory, artificial intelligence, etc.) with impactful computational demonstrations on an application domain.

Topics of interest include, but are not limited to, the following:

• Algorithms for analysis, design, planning, and control of networks;
• Network security, survivability, robustness, risk, and resilience;
• Network applications in emerging technological areas; and
• Case studies involving real, large-scale network problems including publishable data and a computational method contribution.

Manuscripts which apply or develop graphical methods (such as neural networks) for non-network problems are outside the scope of this area.

Quantum Computing

Giacomo Nannicini
Daniel J. Epstein Department of Industrial and Systems Engineering
Ming Hsieh Department of Electrical and Computer Engineering
University of Southern California
Los Angeles, California, USA
[email protected]

The Quantum Computing Area publishes works at the intersection of quantum computing (QC) and operations research (OR). The rapid development of quantum technologies benefits from the application of traditional OR techniques in different phases of chip design and operation; examples are uncertainty quantification, simulation, optimization, modeling. At the same time, algorithmic techniques in these and other areas could benefit from the computational paradigm of quantum computers, which leads to provable speedups under certain conditions. All high-quality research works that study the use of OR techniques for problems arising in the design and operation of quantum computers, and those that study the development of OR techniques that benefit from the QC paradigm (e.g., quantum algorithms for optimization, Monte Carlo simulation, game theory, learning and analytics), will be considered for publication in this area of the INFORMS Journal on Computing. This includes theoretical, computational, and experimental work. Articles related to quantum information theory or quantum computing that have no connection to OR are not considered to be within the scope of the journal.

Simulation, Stochastic Models, & Stochastic Optimization

Shane Henderson
Charles W. Lake, Jr. Professor in Productivity
School of Operations Research and Information Engineering
Cornell University
Ithaca, New York, United States
[email protected]

This area seeks research in computational aspects of the union of the stated areas. The umbrella term “stochastic optimization” should be broadly construed, including, e.g., stochastic dynamic programming, reinforcement learning, stochastic linear and integer programming, and simulation optimization. Both methodological and modeling work is welcome. These areas are mature, so we also welcome papers at the interface of these areas and other methodological areas or applications. Papers that are based primarily on mathematical programming or supervised/unsupervised learning should be submitted to another area of the journal. Clear and concise exposition and rigorous execution are defining elements of successful articles.

Software Tools

Ruth Misener
Imperial College London & Amazon
London, United Kingdom
[email protected]

Software Tools seeks papers describing software and/or data made available to the research community.

In the case of software, the new contribution should be with respect to software, but need not necessarily be so with respect to the underlying mathematics or algorithms. The novelty could be in a sophisticated implementation that highlights state-of-the-art techniques, an ingenious abstraction that simplifies an important and previously difficult research task, or a robust implementation of an existing state-of-the-art algorithm where none previously existed. The software should be of high technical quality; follow best practices for software engineering; and be usable, maintainable, and of long-term interest to the research community. The paper should make the case for the novelty of the software, describe how it fits into the literature, and explain clearly how it can be used to solve a computational problem of importance to the research community. The paper should not be a user’s manual or a technical specification.

In the case of data, the paper should address why the data is important to the research community in advancing research and what methods were used in producing it. The novelty could lie in the way that the data was collected, processed, or cleaned (for example, the data may be high velocity or high volume, requiring sophisticated collection techniques); how it was generated; or how the data was integrated into a novel data analytics workflow to solve a problem of high interest. A paper addressing data may also address associated software.

An archive of the data or software that is the subject of the paper is expected to be made available to the community through the IJOC GitHub repository (https://INFORMSJoC.github.io) following the guidelines described in https://INFORMSJoC.github.io/InstructionsForAuthors.html. Such an archive should be submitted along with the paper for review. There is some expectation that the software will remain useful in the long-term and it should be released under a license that makes it useful for research.

We seek both terse papers, briefly describing a software or data contribution and its importance to the research community, and full expository papers. Short papers focused exclusively on the software or data will be subjected to the usual review process, but are not expected to undergo multiple rounds of revisions. Full-length papers must include additional material with enough significance to merit publication. In both cases, the review process will consist of two separate parts: a review of the software or data to assess the quality and maintainability, and a review of the paper itself to assess the novelty and potential importance to the research community.