Operations Research Letters

  • Volume 1
  • Issue 6
  • ISSN: 0167-6377

Editor-In-Chief: Ward

Next planned ship date: December 18, 2023

  • 5 Year impact factor: 1.2
  • Impact factor: 1.1

Operations Research Letters (ORL) promises the rapid review of short articles on all aspects of operations research. ORL welcomes pure methodological papers and applied papers… Read more

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Operations Research Letters (ORL) promises the rapid review of short articles on all aspects of operations research. ORL welcomes pure methodological papers and applied papers with firm methodological grounding. Click the button below for the full description of the areas the journal covers. Area Editors Approximation & Heuristics Area Editor: Leah Epstein Associate Editors: M. Feldman, J. Hurink, N. Olver, J. Sgall, J. Verschae The area covers all issues relevant to the development of efficient approximate solutions to computationally difficult problems. Examples are heuristic approaches like local search, worst case analysis or competitive analysis of approximation algorithms, complexity theoretic results, and computational investigations of heuristic approaches. Computational Social Science Area Editor: Vianney Perchet Associate Editors: P. Mertikopoulos, A. Drutsa, R. Smorodinsky This area publishes papers focusing on data-driven procedures, either from a theoretical or an applied perspective, in operation research, games, economics and other social science. The scope includes: sample/computational complexity of mechanisms, learning in games/OR/social science, empirical solutions with AI algorithms (such as, but not limited to, deep learning techniques) of complex problems, etc. Continuous Optimization Area Editor: Hector Ramirez Associate Editors: D. Jiang, M.F. Anjos, G. Eichfelder, F. Schoen, D. Orban, L.M. Briceno, D. Dadush Papers in all fields of continuous optimization that are relevant to operations research are welcome. These areas include, but are not restricted to, linear programming, nonlinear programming (constrained or unconstrained, convex or nonconvex, smooth or nonsmooth, finite or infinite-dimensional), complementarity problems, variational inequalities, bilevel programming, and mathematical programs with equilibrium constraints. Financial Engineering Area Editor: Agostino Capponi Associate Editors: D.Mitchell, N.Cai, X. He Financial engineering utilizes methodologies of optimization, simulation, decision analysis and stochastic control to analyse the effectiveness and efficiency of financial markets. This area is interested in papers that innovate in terms of methods or that develop new models which guide financial practices. Examples include but are not limited to Fintech, financial networks, market microstructure, derivative pricing and hedging, credit and systemic risk, energy markets, portfolio selection. Game Theory Area Editor: Tristan Tomala Associate Editors: S. Beal, D.W.K. Yeung, G. Zaccour, V. Ihele This area publishes papers which use game theory to analyze operations research models or make theoretical contributions to the theory of games. The scope includes (but is not limited to): cooperative and non-cooperative games, dynamic games, mechanism and market design, algorithmic game theory, games on networks, games of incomplete information. Graphs & Networks Area Editor: Gianpaolo Oriolo Associate Editors: F. Bonomo, Z. Friggstad, L. Sanita, Y. Faenza The area seeks papers that apply, in original and insightful ways, discrete mathematics to advance the theory and practice of operations research, as well as those reporting theoretical or algorithmic advances for the area. Of particular, but not exclusive, interest are papers devoted to novel applications, telecommunications and transportation networks, graphs and web models and algorithms. Inventory and Supply Chain Optimization Area Editor: Xiuli Chao Associate Editors: S. Zhou, G.J van Houtum, X. Gong, H. Abouee Mehrizi, J. Yang, A. Burnetas, Q. Li The area welcomes innovative papers focused on inventory control and supply management. Examples of topics include, but are not limited to, optimal sourcing, inventory and assortment selection, pricing and inventory optimization, capacity planning, multi-item/echelon systems, algorithms and bounds, near-optimal or asymptotic optimal solutions, and incentive design. Mixed Integer Optimization Area Editor: Marc Pfetsch Associate Editors: L. Liberti, G. Zambelli, J.P. Vielma, R. Fukasawa All submissions advancing the theory and practice of mixed integer (linear or nonlinear) programming like novel techniques and algorithmic approaches in convex relaxations, branch and cut, polyhedral combinatorics and theory driven heuristics are welcome. Case studies may be considered if they contribute to the general methodology. Operations Management Area Editor: Mahesh Nagarajan Associate Editors: T. Huh, C. Shi, L. Chu, N. Golrezaei, R. Roet-Green, D. Saban, Y. Ding The OM department aims to publish short, focused high quality research in the area of operations management, broadly the field of operations research applied to management problems. We welcome papers that use a wide variety of methodologies, both descriptive as well as prescriptive in nature including optimization, applied probability, simulation, and game theory. Scheduling Area Editor: Marc Uetz Associate Editors: E. Pesch, F.C.R Spieksma, B. Moseley, R. Van Stee We seek original and significant contributions to the analysis and solution of sequencing and scheduling problems. This includes structural and algorithmic results, in particular optimization, approximation and online algorithms, as well as game theoretic modeling. Alll results are welcome as long as the relevance of a problem and significance of the contribution is made compellingly clear. Stochastic Models and Data Science Area Editor: Henry Lam Associate Editors: H. Bastani, J. Dong, S. Theja-Maguluri, I. Ryzhov, Y. Zhou The area seeks papers broadly on the interplay between operations research and machine learning and statistics where stochastic variability and uncertainty play a crucial role. The area values both papers that develop or utilize stochastic analysis and computation in data science problems, including but not limited to reinforcement learning, stochastic iterative algorithms for model estimation or training, probabilistic analysis of statistical and machine learning tools, sampling and Monte Carlo methods, and also papers that integrate learning or statistical techniques into stochastic modeling to enhance prediction or decision-making for a wide variety of systems. Stochastic Networks and Queues Area Editor: Jamol Pender Associate Editors: H. Honnappa, W. Wang, Y. Zhao, E. Ozkan The area seeks papers that contribute to the modeling, analysis or innovative application of stochastic networks or queues. Work submitted should propose original models and develop novel analytical or computational methods more than incremental extensions. Examples of relevant application areas include but are not limited to supply chain management, manufacturing, financial engineering, healthcare, revenue management, service operations, telecommunications, sharing economy, online markets and public sector operations research. Application-oriented papers should demonstrate direct practical impact and have a strong methodological component as well. Stochastic Optimization and Machine Learning Area Editor: Wolfram Wiesemann Associate Editors:, M. Claus, A. Georghiou, E. Feinberg, P. Vayanos, G. Yi Ban The Stochastic Optimization and Machine Learning area of Operations Research Letters solicits original articles that generate novel insights into problems that arise in optimization under uncertainty and in machine learning. The focus is broad and encompasses, among others, stochastic (dynamic) programming, (distributionally) robust optimization, data-driven optimization as well as the interface of machine learning with traditional areas of operations research. Successful submissions in this area are expected to make a clear and meaningful academic contribution, which may be through the study of new problems, models, solution techniques, performance analysis and convincing and reproducible numerical evaluations. Advisory Board: Jan Karel Lenstra, Nimrod Megiddo, Peter Glynn