Operations Research Letters

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, K. Elbassioni, M. Chrobak
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: Ning Cai
Associate Editors: D.Mitchell, 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: Sean Zhou
Associate Editors: 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, 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, K. Murthy, 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: Angelos Georghiou
Associate Editors:, M. Claus, E. Feinberg, P. Vayanos, G. Yi Ban, M. Bodur
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