Please note this journal’s peer review system has changed, it now uses Snapp (Springer Nature’s Article Processing Platform). See the journal updates page for more information.

Operations Research Forum is a journal that serves the Operations Research community by addressing a broad range of topics, perspectives, methodologies, and industry applications to foster communication among academics and practitioners, theory and application, and a variety of disciplines (e.g., applied mathematics, computer science, business and economics, and engineering).

The journal covers the entire spectrum of topics, perspectives, methodologies, and industry applications in Operations Research, including, but not limited to:

  • Algorithms
  • Analytics
  • Artificial Intelligence
  • Computational Economics
  • Data Mining
  • Data Sciences
  • Discrete Mathematics
  • Financial Engineering
  • Forecasting
  • Linear Programming
  • Logistics
  • Optimization (Mathematical, Robust, Stochastic)
  • Machine Learning
  • Management Science
  • Mathematical Programming
  • Networks
  • Scheduling
  • Simulation
  • Supply Chain Management
  • Sustainability
  • Theoretical Computer Science

with applications in a broad range of industries, including Education, Energy, Environment, Health Care, Manufacturing, and Transportation.

Article types, reflecting the diversity of the community and the types of contributions to the field, include:

  • original research articles
  • surveys/expositions
  • short communications
  • book reviews
  • reports on computational studies
  • case studies
  • tutorials
  • presentations of new and innovative practical applications
  • pre-registration of experiments (through which either positive or negative results may be reported)

Operations Research Forum encourages the submission of videos, letters to the Editors (opinions and commentaries), interviews, observations on timely topics, and other supplementary electronic materials designed to enhance reader engagement. Of particular interest are contributions that identify and critically discuss trends or contribute to the public’s understanding of OR—its motivations, its results, its impact. In its commitment to promoting education, the journal welcomes submission of articles from students and their mentors.

The journal is committed to being an efficient enterprise to serve the community. We strive for a constructive peer-review process to be conducted in a timely fashion, with all accepted articles immediately being assigned to a specific volume upon publication.

In addition to direct submissions, Operations Research Forum also considers papers that have been referred from Springer Nature’s prestigious Operations Research, Optimization, and Management Science journals portfolio.

 

  • Broad-based journal for the entire Operations Research community covering perspectives, methodologies, and applications
  • Offers a variety of articles types, including research articles, case studies, tutorials, review articles, pre-registration of experiments, and editorials, in order to encourage innovation and engagement
  • Committed to an efficient and constructive peer review process, with all accepted articles being assigned to a volume immediately upon publication
  • No color or page charges, free submission, and is free to access for the first two years of publication
  • Opportunities to publish Topical Collections on emerging topics and issues in the field

Journal information

Editors-in-Chief
  • Marco Lübbecke,
  • Panos M. Pardalos
Publishing model
Hybrid (Transformative Journal). How to publish with us, including Open Access

Journal metrics

42 days
Submission to first decision (Median)
49,605 (2021)
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Latest articles

This journal has 40 open access articles

Journal updates

  • SNAPP – A New Manuscript Submission system

    Snapp (Springer Nature’s Article Processing Platform) is our new peer review platform, replacing the previous system, Editorial Manager.

    SNAPP logo
  • Call for Papers: Hybrid AI – Where data-driven and model-based methods meet

    Data-driven machine learning approaches have been very successful the last 10-15 years. At the same time there are many challenges such as how to deal abstract and causal aspects, how to make learning work with significantly less data like humans can do, and how to achieve robust systems which provides formal guarantees and interpretability. Traditional model- or knowledge-based methods are designed to deal with many of these issues, effectively dealing with generality, abstraction, and causality with strong formal guarantees. A current trend in AI and machine learning today is therefore how to combine these different approaches in a principled and effective way. This is often called hybrid AI.

    During the autumn of 2022 the strategic research environment ELLIIT and Linköping University in Sweden are hosting a 5-week focus period named Hybrid AI – Where data-driven and model-based methods meet. Specific topics are optimisation for learning, learning for optimisation, and statistical-relational approaches to planning, control and decision-making. The main purpose of this topical collection is to encourage publications from interdisciplinary work initiated during this focus period, but other contributions addressing hybrid AI within the intersection between machine learning, optimisation and automatic control are also welcome.

    Submission Deadline: March 15th, 2023

    Guest Editors: 
    Elina Rönnberg, Linköping University (elina.ronnberg@liu.se)
    Anders Hansson, Linköping University
    Fredrik Heintz, Linköping University

  • Call for Papers: Public Transport Optimization: From Theory to Practice (Submission Deadline: December 31, 2022)

    This Special Issue addresses the challenges posed by real-life applications in public transport, and the novel approaches that can effectively tackle them with a special focus on the perspective of the transport companies. On one hand, the goal is to collect new methods that are/can be applied in practice showing their usefulness and encouraging public transport companies to more deeply take advantage of OR approaches. On the other hand, giving visibility to the needs of companies and practitioners could help the academic community better understand and identify promising future research directions.

    Submission Deadline: December 31, 2022

    Guest Editors:

    Valentina Cacchiani, University of Bologna, Italy
    Matthias Müller-Hannemann, Martin Luther University Halle-Wittenberg, Germany
    Federico Perea Rojas-Marcos, University of Seville, Spain

    Download full details here:

    Public Transport Optimization: From Theory to Practice (PDF)

  • Operations Research Forum accepted into Scopus

    We're delighted to announce that Operations Research Forum has been accepted into the Scopus database!

    New Content Item
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About this journal

Electronic ISSN
2662-2556
Abstracted and indexed in
  1. Baidu
  2. CLOCKSS
  3. CNKI
  4. CNPIEC
  5. Dimensions
  6. EBSCO Discovery Service
  7. Google Scholar
  8. Japanese Science and Technology Agency (JST)
  9. Mathematical Reviews
  10. Naver
  11. Norwegian Register for Scientific Journals and Series
  12. OCLC WorldCat Discovery Service
  13. Portico
  14. ProQuest-ExLibris Primo
  15. ProQuest-ExLibris Summon
  16. Research Papers in Economics (RePEc)
  17. SCOPUS
  18. TD Net Discovery Service
  19. Wanfang
  20. zbMATH
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