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Area Editors' Statements
Operations Research publishes material that covers the entire spectrum of problems of interest to the community. For details, please review the statement by each area editor:
The role of the Computation, Data, and Software (CDS) area at Operations Research is two-fold. First, the area editor for CDS has primary responsibility for developing and maintaining standards for publication of empirical research, as well as for developing and maintaining procedures for reviewing such research. These standards and procedures will apply across all areas of the journal, with the high-level goal of ensuring best practices for performing empirical science are followed in all publication. Second, the area editor for CDS also has the responsibility to oversee the implementation of the procedures for review and to participate in the review process in partnership with other area editors. The high-level vision is to be able to rigorously assess empirical work, which requires both the ability to reproduce results, and the assessment of the empirical testing performed.
Reproducibility. The ability to replicate published empirical results is a fundamental tenet of empirical scientific research that dates back centuries. In recent years, there has been a slow realization across many fields that standards for replicability have not been enforced at an acceptable level. Research involving computations is particularly difficult to replicate for many reasons and it is not a simple undertaking to ensure that results published in Operations Research are indeed replicable. Computations are affected by many sources of variability, some of which can be controlled and others of which cannot. Ability to replicate a set of experiments is affected not only by these sources of variability, but also by more mundane challenges, such as a lack of availability of proprietary data or software. In developing standards for replicability, it is important to not only define what replicability means, but also to determine what reasonable practical requirements should be enforced.
Empirical Testing Assessment. Verifying replicability is just the first step in ensuring the highest level of rigor in the empirical analyses that appear in Operations Research. It is not only important that experiments be replicable but also that conclusions drawn based on those experiments are valid from a scientific standpoint. This can mean many different things, including that the analysis acknowledges and controls for sources of variability, eliminating those that can be easily controlled and accounting for those that cannot with rigorous statistical analysis. Assessing the validity of conclusions drawn based on empirical data also involves determining the degree to which results can be generalized beyond the datasets used for the analysis and whether statements made in the paper are consistent with this assessment. The appropriateness of the dataset for the task of answering the scientific questions raised in the paper is also an important part of assessing the scientific validity of empirical results.
Achieving the highest level of rigor in published empirical research is not something that will happen all at once and the goal of this editorial area is to evolve these standards and procedures slowly over the long term, ensuring the community has sufficient time to educate themselves about compliance and to transition their research workflows as appropriate.
Note that manuscripts are not submitted directly to this Area.
Associate Editors: Federico Battista, Drew Philip Kouri, Benoît Legat, Johannes Milz, Sara Shashaani, Juan Pablo Vielma, Heng Zhang
The Decision Analysis area encourages papers that improve our understanding of decision making, particularly in problems where uncertainty or conflicting objectives play an important role. Decision analysis researchers often distinguish between normative research that studies how people should make decisions (e.g., following subjective expected utility models) and descriptive research that examines how stakeholders make decisions, particularly regarding deviations from normative ideals. The area focuses on prescriptive research that concerns how to help decision makers implement these normative ideals in practice or improve our understanding of optimal decision making.
Papers may provide methodological contributions or study a specific problem. Papers providing methodological contributions should extend, unify, or improve upon existing methods or describe a novel approach. Decision analysis methods include tools for modeling uncertainty (e.g., probability elicitation, Bayesian inference, or data analytics), modeling preferences (e.g., modeling risk preferences or multiattribute utility modeling), and representing and solving decision problems (e.g., using dynamic programming or optimization). Problem-oriented contributions should study a decision problem of significant interest: such papers may consider a novel application or use novel methods to provide insightful results.
Decision Analysis papers may draw on related fields such as the psychology of judgment and choice (e.g., behavioral or experimental papers involving heuristics and biases) or methods for dealing with multiple stakeholders (e.g., game theory or negotiation). However, the ultimate goal should be prescriptive, i.e., improving decision making or our understanding of optimal decisions.
For all papers, the contributions should be significant, relevant, and conceptually sound, in addition to being of interest to the decision analysis and broader operations research communities.
Associate Editors: Manel Baucells, David Bell, Soo-Haeng Cho, Sebastian Ebert, Negin Golrezaei, Yael Grushka-Cockayne, Fabio Maccheroni, Velibor Mišić, Selva Nadarajah, Anton Ovchinnikov, Richard Peter, Ville Satopaa, Marco Scarsini, Canan Ulu, Hao Zhang, Spyros Zoumpoulis
The Energy and environment Area invites papers that address important challenges in this area, through operations research models and techniques. Such challenges are likely to require insights from multiple disciplines (economics, statistics, environmental sciences and engineering, etc.) so multi-disciplinary papers are welcome.
Topics of interest include:
- Design or analysis of policies, trading schemes and markets that facilitate meeting the decarbonization goals set in many jurisdictions;
- Novel operations and design problems arising from innovations in sustainable development, transportation, agriculture, cities, green chemistry, and renewable energy;
- Planning and control for natural resource systems including fisheries, forests, and water resources, and in forward and reverse supply chain management;
- Design of advanced systems or governance policies for energy management with increasing digitization, e.g. smart grids;
- Planning and control of systems in energy-related industries, such as capacity planning and the operation of large-scale networks for gas and electricity;
- Policy analysis in the above areas that recognizes operational and system design factors that impact policy outcomes and that make concrete policy recommendations.
- Frameworks that address equity and justice within energy and environment.
In a cover letter, submitting authors should briefly describe their paper’s main contributions. Four types of contributions are of particular interest. These are: 1) application of operations research in a real-world setting to substantially improve policy or management, with measurement of the social, environmental and economic implications. 2) A methodological innovation in operations research motivated by important aspects of an environmental, sustainability or energy challenge. 3) Consolidation and insightful appraisal of past work. (A paper should address the relevant literature from other disciplines, not only OR.) 4) Data gathering, e.g. publicly available benchmark repositories of benefit to the OR energy and environment modeling and analysis community.
Associate Editors: Mette Bjørndal, Valentina Bosetti, Hongyue Jin, Jalal Kazempour, Anthony Papavasiliou, Geoff Pritchard, Nicola Secomandi, Afzal Siddiqui, Ramteen Sioshansi
Financial Engineering
Area Editors: Agostino Capponi and Xuedong He
Financial Engineering concerns the design and integration of mathematical models and methods for the analysis of financial markets, services, and regulations. Fundamental operations research tools of optimization, stochastic modeling, simulation, dynamic programming, mechanism design, and game theory drive much innovation in the financial engineering area and enjoy wide use by finance industry firms, regulatory agencies, and other organizations that employ financial markets to manage capital and risk. Past research that has generated broad impact include portfolio optimization methods, stochastic models and simulation methods for derivative pricing and risk analysis, and dynamic programming methods for market making and trade execution. The evolving nature of financial markets give rise to new challenging problems, ranging from the design of Fintech technologies to the developed and customization of AI analytics into financial services.
We invite papers that advance the state-of-the-art of models, methods, and their application. We especially encourage submission of papers that address emerging needs such as the mechanism design of decentralized ledgers, the microstructure of decentralized exchanges and related DeFi applications, the design of machine learning and AI algorithms for pricing and risk management, the measurement and valuation of systemic and operational risk; as well as the engineering analysis of financial innovation and new marketplaces. We also encourage submission of papers that make major methodological advances that are of broad interest to financial engineering researchers and practitioners. Papers should represent original contributions that significantly advance the theory or practice of financial engineering.
Associate Editors: Nan Chen, Lin William Cong, Min Dai, Damir Filipović, Kay Giesecke, Steve Kou, Markus Pelger, Gerry Tsoukalas, Luitgard Veraart, Ruodu Wang, Xunyu Zhou
The Machine Learning and Data Science Department serves as a vibrant hub for cutting-edge research at the intersection of operations research, machine learning, and data science, with a strong emphasis on addressing complex business and societal challenges and providing managerial insights. We are particularly interested in submissions that advance the theoretical understanding of data science methods, demonstrate clear real-world impact, or provide valuable empirical managerial insights.
Our department actively seeks submissions with original theoretical results on machine learning algorithms, optimization methods that exploit the randomness of the data, and probability theory with implications to data science, as well as more fundamental statistical contributions such as relevant statistical properties of estimators for a desired application. Moreover, we appreciate statistical insights that deepen our grasp of operational applications. In contrast to papers submitted to mainstream journals in statistics, econometrics, and optimization, contributions to Operations Research should endeavor to offer managerial insights and showcase the practicality of proposed methods or models in addressing business and operational challenges.
In addition, we are also keenly interested in empirical and applied operations work, including but not limited to applied machine learning, structural estimation or reduced-form empirical studies of operations problems, and behavioral operations experiments. We especially value contributions that bridge the gap between theory and practice.
Associate Editors: Hamsa Bastani, Robert Bray, Matias Cattaneo, Jinyuan Chang, Yuxin Chen, Ruomeng Cui, Andrew Davis, Ethan Fang, Kris J. Ferreira, Jeff Hong, Tengyuan Liang, Lauren Lu, Jinchi Lv, Rahul Mazumder, Veronika Rockova, Cong Shi, Hummy Song, Weijie Su, Stefan Wager, Yining Wang, Zhaoran Wang, Zizhuo Wang, Yao Xie, Yuqian Xu, Heng Zhang, Renyu Zhang, Yuan Zhou, Zhengyuan Zhou
Area Editors: René Caldentey and Ilan Lobel
The Markets, Platforms, and Revenue Management area is dedicated to advancing the theory and practice of revenue management and pricing analytics, as well as the design and operation of markets and platforms. In an era characterized by rapid technological advancements in information, communication, and computing, our economic interactions are undergoing significant transformations. These developments facilitate the collection of detailed data on customer and firm behavior, and also enable highly refined optimization of market outcomes. As a result, firms and market operators now possess unparalleled control over various aspects of market transactions. This control spans from pricing and revenue optimization to the creation of matching markets and the design of mechanisms that regulate them.
The evolving landscape of markets and online platforms across various industries is driving a shift in academic research toward enhancing our understanding of how these markets function and the development of more sophisticated models to describe the activities they support. These activities encompass efficient and fair resource allocation, real-time pricing analytics (e.g., as in ride-sharing and ad auctions), personalized assortment, dynamic matching algorithms, real-time learning capabilities, digital advertising, and social media rating and review systems, among others.
The Markets, Platforms, and Revenue Management area seeks to publish impactful research that addresses real-world problems in these and related fields, with an emphasis on substantial methodological or analytical contributions. This includes advanced modeling, econometrics, empirical studies, behavioral modeling, and innovations in algorithms. We also welcome papers that excel in solving relevant practical problems, supported by well-documented numerical studies, ideally based on real data.
Associate Editors: Santiago Balseiro, Hamsa Bastani, Martin Bichler, Kostas Bimpikis, Ozan Candogan, Antoine Desir, Adam Elmachtoub, Jake Feldman, Santiago Gallino, Negin Golrezaei, Yonatan Gur, Ming Hu, Stefanus Jasin, Yash Kanoria, Jussi Keppo, Bora Keskin, Jun Li, Brendan Lucier, Ali Makhdoumi, Azarakhsh Malekian, Vahideh Manshadi, Saša Pekeč, Daniela Saban, Nicolas Stier-Moses, Zizhuo Wang, Dan Zhang
Area Editor: Rouba Ibrahim and Francis de Véricourt
The Operations and Supply Chains area considers articles at the forefront of advancing the science, management, and engineering of operations, encompassing research on the design, planning, control, and improvement of service, healthcare, supply, platform, and distribution operations. We encourage papers that examine the interface of operations with other areas, including marketing, finance, accounting, economics, strategy, entrepreneurship/innovation, machine learning, and behavioral science.
Papers in the Operations and Supply Chain area of Operations Research can either have methodological contribution, practical contribution, or somewhere along the efficient frontier between the two. In general, if an application area is new and interesting then there will be a lower bar for methodological contribution. Whereas, if the application area is old (e.g., standard inventory theory) then there will be a very high bar for methodological contribution. We particularly welcome papers that blend methodologies, for example work that combines mathematical models with behavioral experiments or with the analysis of real-life data. In all cases, however, the contribution must be significant, relevant, and conceptually sound.
In terms of methodological contribution, we welcome all quantitative methodologies. In particular, we welcome the development of new mathematical models and methods for critical or emerging operational issues, the development of new methods for the empirical analysis of operational phenomena, and/or the incorporation of modern technological advances of business analytics in operations. Papers without any propositions or theorems are unlikely to proceed to review. In terms of practical contribution, we welcome both a management and an engineering view of this. Managerial insights are welcome but should be of real significance to managers. Engineering solutions are welcome but then should be for real rather than artificial environments. Papers may include motivation from industrial practice, validation with real-world data, and/or computationally tractable algorithms.
Associate Editors: Hyun-Soo Ahn, Saed Alizamir, Ravi Anupindi, Gah-Yi Ban, Opher Baron, Damian Beil, Fernando Bernstein, Xiuli Chao, Xin Chen, Ying-Ju Chen, Mabel Chou, So-Yeon Chun, Maxime Cohen, Gregory DeCroix, Lingxiu Dong, Adam Elmachtoub, Ming Hu, Tim Huh, Song-Hee Kim, Mirko Kremer, Adam Mersereau, Kevin Shang, Nur Sunar, Terry Taylor, Jordan Tong, Huseyin Topaloglu, Nikos Trichakis, Joline Uichanco, Shouqiang (Qiang) Wang, Leon Zhu
Area Editors: Samuel Burer and Dan Iancu
Optimization is a foundational pillar of operations research and continues to grow in its ability to model and solve real-world problems. Operations Research is thus committed to publishing new and significant advances in optimization.
The Optimization area of Operations Research invites submissions of high-quality manuscripts covering all areas of optimization, including—but not limited to—combinatorial, convex, nonlinear, integer, stochastic, or robust optimization, as well as all aspects of dynamic optimization (e.g., MDPs, approximate dynamic programming, reinforcement learning, online optimization, etc.). We also encourage submissions that connect with other areas within Operations Research, provided that the main contribution is substantially based on or advances knowledge within Optimization.
Submitted papers will be evaluated along multiple dimensions, including their contribution to modeling, theory, algorithms, computation, or applications. To warrant publication, a paper should excel in at least one—but not necessarily all—of these dimensions, and should be clear, concise, and relevant to a broad audience. Before submission, authors should carefully consider the tradeoff between contributions and length: papers that exceed the page limit will only be considered in exceptional circumstances, whereas shorter—but high-quality—manuscripts are strongly encouraged. The evaluation of all submissions will consider the ratio of contributions to length.
All submissions should adhere to the guidelines of Operations Research, including page-limit and proceedings policies.
Associate Editors: Selin Ahipasaoglu, Alper Atamtürk, Santiago Balseiro, Frank Curtis, Erick Delage, Paul Grigas, Vishal Gupta, Grani Hanasusanto, Alexandre Jacquillat, Fatma Kılınç-Karzan, Çağıl Koçyiğit, Simge Küçükyavuz, George Lan, Ivana Ljubic, Will Ma, Rahul Mazumder, Velibor Mišić, Peyman Mohajerin Esfahani, Viswanath Nagarajan, Karthik Natarajan, Danny Segev, Siqian Shen, Kim-Chuan Toh, John Turner, Madeleine Udell, Willem-Jan van Hoeve, Phebe Vayanos, László Végh, Juan Pablo Vielma, Yehua Wei, Weijun Xie
Area Editor: Turgay Ayer
In the ever-evolving field of Operations Research, a commitment to practical relevance and the resolution of real-world problems is paramount. This area, now known as "Real World OR Innovations," serves as a platform for the publication of high-quality research that epitomizes the practical impact achieved through innovative applications of operations research (OR) methods.
The primary objective of this area is to feature research studies that have made a tangible practical impact in an industry or society through the innovative development, utilization, and/or application of OR methods. The underlying belief is that the true essence of OR lies in its ability to effect positive change in the real world, and this commitment remains unwavering in the presentation of studies that exemplify this transformative power.
Criteria for Publication:
To uphold the highest standards of quality and relevance, clear criteria have been established for acceptance in "Real World OR Innovations." These criteria are thoughtfully designed to underscore the practical significance and innovation within both OR methods and their applications:
Implementation and Measurable Impact: Submitted papers must provide a detailed account of the actual implementation of an OR solution approach within a firm, organization, or society. Authors should also transparently demonstrate the measurable and quantifiable benefits derived from their OR-based solutions. Compelling evidence of improvements stemming from implementation is imperative.Innovation: This area seeks papers that showcase practical impact through innovation, whether in terms of OR methods or application area. Particular interest lies in papers that venture into uncharted territories within the realm of OR applications. Straightforward implementations of existing OR methods in real-world will not be considered for publication in this area and might be more suitable for other publishing outlets.Verification: Authors are expected to provide verifiable evidence demonstrating the impact of the implementation on decision-making or policies. To substantiate the authenticity of the implementation and the resulting benefits, a verification letter from the relevant organization is required. The letter should describe the problem studied, elaborate on the benefits to the organization and verify the claimed impact.Clarity: Clarity in writing is paramount to ensure that a broad audience, including readers outside academia, can readily comprehend and appreciate the contributions.The submission must also include a one-page executive summary explaining the paper’s key insights and its practical impact in non-technical terms. The target audience for this executive summary is leaders in the relevant industry.
The emphasis remains steadfast on research studies that have unquestionably been implemented and influenced real-world decision-making and/or policies. Research papers that are thoroughly designed for implementation, with all the key pieces in place for success, but are not implemented due to uncontrollable factors might be considered for publication in this area; however, such cases will be exceptional and handled on a case-by-case basis.
Associate Editors: Mehmet Ayvaci, Gemma Berenguer, Margrét Bjarnadóttir, Dick den Hertog, Jérémie Gallien, Gürhan Kök, Jun Li, Tugce Martagan, Stefan Minner, Daniela Saban, Ozge Sahin, Garrett van Ryzin, Phebe Vayanos, Can Zhang
Area Editor: Cole Smith
The field of operations research enjoys a rich tradition of addressing a breadth of important security and defense problems. Many seminal works in these fields have been published by Operations Research within the former area of Military and Homeland Security. This newly renamed area continues the tradition of publishing outstanding operations research papers applied to security and defense applications, broadly construed.
Applications of operations research in security might analyze problems in optimally fortifying infrastructure, protecting systems from adversarial intrusion, and combatting illicit operations, among others. We also encourage papers that study emerging high-tech security applications, e.g., operations research methods applied to cryptography, cybersecurity, artificial intelligence, and autonomous systems. Additionally, this area welcomes papers that study the ability of populations to reliably access critical resources, such as studies on water security, food security, and energy security. Within these studies, threats to security can be conceived of as stemming from malicious and intentional adversaries, or as stemming from natural disasters.
This area continues to strongly encourage the submission of defense-oriented research. These studies may examine battlefield operations from a military perspective, deployment of autonomous systems such as fleets of unmanned vehicles, emerging or classical problems related to military logistics, and so on. Research on homeland security problems is especially appropriate. Such papers might regard threat detection, sensing, patrolling, and surveillance, among other topics.
The major intellectual contribution of papers submitted to this area can be oriented toward the study of a specific application, or instead toward the development of new methodology or theoretical underpinnings that enable the analysis of a problem related to security or defense. It is not necessary for papers to use real data, nor does the paper need to involve a specific industry or government partner. However, papers that regard heavily stylized problems may be more suitable for a different area that examines foundational operations research. We emphasize that submissions to this area must contribute a sophisticated use of operations research: Case study papers that use a routine application of operations research may not be appropriate.
Associate Editors: Laura Albert, Rajan Batta, Thomas Lidbetter, Elise Miller-Hooks, Fernando Ordóñez, Johannes Royset, Paola Scaparra, Siqian Shen, Tauhid Zaman
The Simulation area publishes significant original work in simulation modeling, methodology, and applications that advance the knowledge and practice of simulation.
In the past few decades, methodological research in simulation has focussed on design and improvement of simulation optimization methods and sensitivity analysis, development of algorithms and analysis for ranking and selection of simulated systems, provably efficient simulation methods including variance reduction techniques, input modelling and input uncertainty quantification, output analysis including robustness to model uncertainty, novel sampling algorithms and so on. We continue to welcome state of the art research in the above areas. In addition, we invite contributions that develop the interface of simulation with other methodological areas. For instance, research that combines simulation techniques with some of the recent developments in machine learning and AI are especially encouraged. We also encourage research that uses and enhances core simulation techniques in any application area including but not limited to financial engineering, healthcare, environment and energy. Potentially impactful comprehensive empirical simulation research is encouraged even if it lacks adequate analytical support when it is clear that analysis may be difficult to come by.
Simulation methodology papers should have broad applicability and explain both the need for and the use of the new methodology. Papers that deal with a significant application area must be more than just well-executed simulation studies of a particular problem. They should emphasize modeling concepts or policy implications that can be adapted to many problems within the application domain and/or more widely, and/or demonstrate how general problem structures can enable novel techniques of simulation analysis or implementation to improve the size, complexity, or speed of the analysis. Empirical work should be sufficiently well-described that the results can be repeated up to sampling error, perhaps with the aid of an online companion for complicated models.
Associate Editors: Jing Dong, Shane Henderson, Susan R. Hunter, Kyoung-Kuk Kim, Henry Lam, Karthyek Rajhaa Annaswamy Murthy, Hongseok Namkoong, Ilya O. Ryzhov, Xiaowei Zhang, Zeyu Zheng
Area Editor: Andrew Schaefer
Society’s most important decisions often arise from highly complex situations, which unfold over time and under uncertainty, with multiple stakeholders having competing objectives. The Societal Impact area seeks papers that consider societally important decisions, leading to real insight for practitioners. It also seeks papers that bring new methods to established societally impactful problems in the operations research (OR) literature, leading to novel insight. Papers are generally expected to use historical data to calibrate the models. Coauthorship with domain experts outside OR is particularly welcome. While novel OR methodology is not the primary focus of the area, the use of “off-the-shelf” techniques is not encouraged. The area encourages papers arising in health care, community resilience, sustainability, government and nonprofit services, humanitarian and disaster response, criminal justice, and related areas. Papers that address pressing societal questions that have yet to be considered by the OR community are particularly welcome. Priority will be given to papers that present novel and convincing data-driven model-based analyses of issues likely to generate widespread public interest, awareness, and impact.
Associate Editors : Oguzhan Alagoz, Mustafa Akan, Nilay Tanik Argon, Tim Chan, Sarang Deo, Jérémie Gallien, Archis Ghate, Yongpei Guan, Miguel Lejeune, Nan Liu, Vahideh Manshadi, Soroush Saghafian, Steven Shechter, Pengyi Shi, Greys Sošić, Julie Swann, Andy Trapp
Area Editors: Neil Walton and Raman Randhawa
The Stochastic Models area publishes papers that make significant contributions to the representation and analysis of systems that are intrinsically random.
We welcome papers that propose new models; that develop new methodologies for existing models; that provide new performance insights; or, that improve operational and managerial decision making.
Stochastic modeling is applied in all areas of operations research. Thus, we encourage articles from a broad range of application areas. These include, but are not limited to, service operations, revenue management, manufacturing, supply chain, financial engineering, healthcare, social networks, communication systems, and transportation. Papers on emerging areas are also invited.
Where possible, we encourage papers to have a strong methodological contribution. Example research themes include queueing theory, stochastic optimization, stochastic process limits, Markov decision processes & reinforcement learning, random graphs, statistics learning theory, game theory, and simulation & uncertainty quantification. We encourage authors of papers with a strong technical contribution to highlight the key insights of their work so as to introduce their contributions in a manner that is accessible to a broad operations research readership.
Furthermore, we encourage practical articles where the novel research analysis leads to a strong impact on practice either through direct operational decision making, or by developing tools through stochastic modeling.
In making publication decisions, the editorial board will consider the importance of the systems being modeled, the originality of the modeling and analysis, the quality of the results, the clarity of the exposition, and thus, the overall utility of the work to the OR/MS community.
Associate Editors: Ali Aouad, Alessandro Arlotto, Achal Bassamboo, Kostas Bimpikis, Xinyun Chen, Andrew Daw, Jing Dong, Varun Gupta, John Hasenbein, Shuangchi He, Thodoris Lykouris, Siva Maguluri, Alexandre Proutiere, Dan Russo, Pengyi Shi, Johan van Leeuwaarden, Maaike Verloop, Yehua Wei, Linwei Xin, Jiaming Xu
Area Editor: Jose Correa
Transportation was one of the earliest application areas of operations research, and important transportation problems, such as the traveling salesman problem, vehicle routing problem, and traffic assignment problem, contributed to fundamental knowledge in operations research. Transportation remains one of the most important and vibrant areas of operations research.
We welcome papers that address any aspect of transportation and its connections to operations research, including modern topics that have not traditionally appeared in the journal. This, in particular, means that the problem addressed should be stated precisely and thoroughly analyzed either theoretically, numerically, or experimentally. Ultimately, the main evaluation criterion is significance. Problems that interest a larger part of the operations research community are preferred over problems that interest only a small group.
While with a strong mathematical foundation, transportation is primarily an application area of operations research, and therefore, emphasis should be placed on good modeling and relevance to real transportation problems. The analysis may involve any appropriate methodology, including techniques from statistics, optimization, game theory, probability, dynamical systems, or numerical methods. Problems and approaches that are innovative and new to the community are especially welcome. Numerical work and experimental work should be described sufficiently precisely to enable replication of the work. Appendices that facilitate replication of the work, including mathematical proofs, code, and data sets, are welcome.
Papers should clearly explain the contribution of the paper in the introduction. The paper should state exactly what is new, and if relevant, the contribution of the paper should be compared with the existing literature. The explanation of the contribution should be sufficiently precise to enable an unambiguous verification of whether the contribution claim is correct.
Associate Editors: Nicole Adler, Roberto Baldacci, Michel Bierlaire, Merve Bodur, John Carlsson, Daniel Freund, Tobias Harks, Anton Kleywegt, Nicole Megow, Neil Olver, Carolina Osorio, Milind Sohoni, Cathy Wu, Zhou Xu, Chiwei Yan