Operations Research Letters

Operations Research Letters - ISSN 0167-6377
Source Normalized Impact per Paper (SNIP): 0.966 Source Normalized Impact per Paper (SNIP):
SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.
SCImago Journal Rank (SJR): 0.661 SCImago Journal Rank (SJR):
SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal’s impact.
Impact Factor: 1.154 (2020) Impact Factor:
The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years.
© 2017 Journal Citation Reports ® (Clarivate Analytics, 2017)
5 Year Impact Factor: 1.175 (2020) Five-Year Impact Factor:
To calculate the five year Impact Factor, citations are counted in 2016 to the previous five years and divided by the source items published in the previous five years.
© 2017 Journal Citation Reports ® (Clarivate Analytics, 2017)
Volumes: Volume 1
Issues: 6 issues
ISSN: 01676377
Editor-in-Chief: Ward

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Operations Research Letters is committed to the rapid review and fast publication of short articles on all aspects of operations research and analytics. The traditional strength of ORL is methodology. ORL welcomes both pure methodological papers and more applied papers, as is reflected in the Area Editor statements below. To recognize more recent interest at the intersection of Data Science and Operations Research, the journal also welcomes data science papers that involve new operational tools, operations papers in which statistical estimation and calibration questions arise, and empirical papers with a significant operations component, which would be handled by the EiC, most likely using one of the at-large associate editors. A similar process will be followed for papers in healthcare operations, another area in which the journal is looking to expand.

Area Editors

Stochastic Optimization and Machine Learning
Area Editor: Wolfram Wiesemann
Associate Editors: D. Kuhn, M. Claus, A. Georghiou, E. Feinberg, P. Vayanos
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.

Continuous Optimization
Area Editor: Hector Ramirez
Associate Editors: D. Jiang, M.F. Anjos, G. Eichfelder, F. Schoen, D. Orban, L.M. Briceno
Updated Area Statement: 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.

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.

Computational Social Science
Area Editor: Vianney Perchet
Associate Editors: P. Mertikopoulos, A. Drutsa,
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.

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.

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.

Graphs & Networks
Area Editor: Gianpaolo Oriolo
Associate Editors: A. Gupta, F. Bonomo, 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. Lai, G.J van Houtum, X. Gong, H. Abouee Mehrizi, J. Yang, A. Burnetas
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.

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
Area Editor: John Hasenbein
Associate Editors: J. Pender, G. Nguyen, J.P. Kharoufeh
The area seeks papers dealing with models in which stochastic variability and uncertainty play a crucial role, with networks and queueing systems being one main example. The area values papers that propose original models and develop novel analytical or computational methods more than incremental extensions. Application-oriented papers should have a strong methodological component.

Financial Engineering
Area Editor: Agostino Capponi 2020
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

Approximation & Heuristics
Area Editor: Gerhard Woeginger
Associate Editors: C. Hurkens, J. Sgall, J.L. Hurink, N. Olver, A. Levin
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.

"At-Large" Associate Editors: Sarang Deo, Jing Dong, Henry Lam

Advisory Board: Jan Karel Lenstra, Nimrod Megiddo, Peter Glynn