# IJOC — INFORMS Journal on Computing

- **Publisher**: INFORMS
- **ISSN**: 1091-9856
- **Impact factor**: 2.1
- **CCF rank**: B
- **Trackers**: 0
- **Canonical page**: https://www.myhuiban.com/journal/692

## Call for papers

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. Applications in Biology, Medicine, & Healthcare Computational Modeling: Methods & Analysis Data Science & Machine Learning Design & Analysis of Algorithms - Continuous Design & Analysis of Algorithms - Discrete Heuristic Search & Approximation Algorithms Network Optimization: Algorithms & Applications Simulation Software Tools Stochastic Models & Reinforcement Learning Applications in Biology, Medicine, & Healthcare J. Paul Brooks Supply Chain Management and Analytics Virginia Commonwealth University Richmond, Virginia, USA jpbrooks@vcu.edu The frontiers of biological, medical, and healthcare research depend on sophisticated modeling, analysis, and computational techniques. Operations research, including all aspects of optimization, stochastic processes, and simulation, are vital tools for investigating complex biological systems, advanced medical procedures, and healthcare delivery processes. In turn, the computational challenges associated with these applications have spawned new developments in operations research, creating a synergy of application, theory, and implementation. Submissions that include advancements in OR methodology for addressing a problem in biology, medicine, and/or healthcare are welcome. In addition, we welcome submissions presenting innovative applications of existing OR methodologies to address new problems in their respective domains. Submissions should include aspects of biology/medicine/healthcare, operations research/management science, and computer science/computing. Accepted papers will provide a significant contribution in one of these three aspects or possibly provide a contribution in some combination of the three. Accordingly, reviewers may be selected from multiple disciplines to provide complementary perspectives in evaluating submissions. Examples of computational OR applied to biology/medicine/healthcare: computational OR methods for improving healthcare delivery development of OR-based machine learning algorithms for biomedical data scheduling of healthcare workers and patients advancements in the use of optimization for radiation treatment therapy modeling of infection control and antibiotic stewardship programs the use of OR methodology supporting the establishment of healthcare policy novel OR-based algorithms for computational biology Computational Modeling: Methods & Analysis Pascal Van Hentenryck H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, Georgia, USA pascal.vanhentenryck@isye.gatech.edu Rapid advances in computing technology have permitted the solution of increasingly large and complex operations research models. As a consequence, much greater attention must be paid to formulation, modeling, and computational issues. This includes the need to understand how to model complex problems effectively, how to compare formulations, and how to find the proper tradeoffs between model fidelity and the required computational resources to solve it. The area also covers the need for novel techniques and tools for acquiring, formulating, debugging, analyzing, and visualizing models. Modeling Methods and Model Analysis are the two main overlapping themes in this very broad area: Modeling Methods covers research on ways of creating and managing operations research models which feature a significant computational component. It 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. Additionally, this theme includes how to formulate and compare models for highly complex applications, trading off model fidelity and computational requirements. Optimization for novel computational paradigms such as quantum computing is also considered. Model Analysis covers research on ways to analyze models and model instances to provide useful insights. Examples include computational methods and tools for explaining, debugging, visualizing, and analyzing models. It also covers techniques for reformulating and simplifying models, and for infeasibility analysis. Data Science & Machine Learning Ram Ramesh Department of Management Science and Systems School of Management SUNY at Buffalo Buffalo, New York, USA rramesh@acsu.buffalo.edu In our increasingly information-based economy and society, data-driven decision-making and data-driven automation have become central to most human endeavors. Data Science is rooted in the domain of data-driven decision-making and Machine Learning is centered on data-driven automation. Data Science spans the four dimensions of the analytics field – diagnostic, descriptive, predictive, and prescriptive analytics. Similarly, Machine Learning is focused on modeling data, learning from data, and applying them in the automation of processes. Although their target application domains could be different, Data Science and Machine Learning employ similar models, algorithms, and systems, and both are well established in the underlying disciplines of statistics, optimization, and computer science. A common thread that unifies the two fields is their consistent thrust on extracting knowledge and insights from possibly noisy data, and applying this knowledge and data-supported insights to solve both structured and ill-structured problems in a variety of application domains. The explosive growth in the fields of statistical modeling, optimization, algorithm design, and computing systems have greatly enhanced the scope and reach of Data Science and Machine Learning in contemporary economy and society. The Data Science & Machine Learning (DSML) area provides a forum for publication of papers on the cutting edge of these rapidly evolving disciplines. The area welcomes innovative contributions with a strong emphasis on analytical modeling, algorithmic development, and computational experimentation leading to validation. Given the multi-disciplinary scope of the journal focusing on the interface of computer science, information systems, and operations research, topics that are most appropriate include: Data modeling and learning approaches that demonstrate significant performance or functionality improvements Methods for efficiently managing distributed data or knowledge Innovations in the use of DSML methodologies in important application domains (to name a few, consumer behavior, preference analysis, social media, financial technologies, GIS, agent-based applications) Innovations in DSML methodologies leading to novel and superior solutions to both structured and ill-structured problems (to name a few, data mining, link prediction, knowledge discovery, graph modeling, topic modeling, statistical modeling) Advances in methods for analyzing performance of DSML systems Design & Analysis of Algorithms - Continuous Antonio Frangioni Department of Computer Science Università di Pisa Pisa, Italy frangio@di.unipi.it Design & Analysis of Algorithms - Discrete Andrea Lodi Department of Mathematical and Industrial Engineering Ecole Polytechnique de Montréal Montréal, Canada andrea.lodi@polymtl.ca The Design & Analysis of Algorithms Areas seek to publish significant contributions to the algorithmic aspects of operations research. The development of algorithms for optimization problems is a vibrant field due to the need, on one hand, to develop general-purpose solvers capable to tackle as large as possible classes of optimization problems, and, on the other hand, to exploit as much as possible the structure of the problem at hand to improve the effectiveness and efficiency of the approach. This is especially relevant when the problems are of large scale and/or tight constraints are imposed on the available computational resources, as it happens in many applications. Although a single objective function is most often considered, solutions methods for multi-objective optimization problems, equilibria/variational ones and games are also welcome. Thus, the design of algorithms covers a broad spectrum of issues, ranging from the study of the complexity or approximability of a problem to an algorithmic engineering project involving high-performance computing platforms, advanced data structures, and real-life data. The analysis of algorithms may concern all the nontrivial aspects related to their completeness, correctness, and efficiency as expressed by the trade-off between the solution quality they deliver and the amount of computational resources they require, estimated by either theoretical (worst-case or probabilistic) or experimental means. In principle, algorithms covering all possible applications (resource allocation, facility location, routing and scheduling, energy, transportation, finance, just to mention a few) and of all possible types (exact, heuristic, continuous, combinatorial, etc.) are welcome in these two areas, subject to the specificities of each detailed below. However, other areas of the Journal focus on either specific applications (Biology, Medicine, & Healthcare; Knowledge Management & Machine Learning; Network Optimization) or on specific solution techniques (Stochastic Models; Heuristic Search; Simulation). In general, a research pape

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