SN 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:
- Artificial Intelligence
- Computational Economics
- Data Mining
- Data Sciences
- Discrete Mathematics
- Financial Engineering
- Linear Programming
- Optimization (Mathematical, Robust, Stochastic)
- Machine Learning
- Management Science
- Mathematical Programming
- Supply Chain Management
- 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
- short communications
- book reviews
- reports on computational studies
- case studies
- presentations of new and innovative practical applications
- pre-registration of experiments (through which either positive or negative results may be reported)
SN 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, SN 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
- Marco Lübbecke,
- Panos M. Pardalos
- Publishing model
- Hybrid. Learn about publishing OA with us
AuthorsThis is part of 1 collection:
Topical Collections are designed to facilitate the publication of collective research that is either ongoing or time dependent. One of the benefits of a Topical Collection is it gives Guest Editors, Societies, Universities, and Research Groups, their own resource that showcases their research and work in one place.
SN Operations Research Forum welcomes Topical Collections in emerging and hot topics, relevant to the aims and scope of the journal. Topical Collections can also originate from well-established and upcoming conferences or focus on the research legacy of a pioneer in the field as well. We accept proposals for Topical Collections and nominations from Guest Editors.
Editors-in-Chief, Marco Lübbecke and Panos Pardalos, share their insights on the inception of the journal and its unique positioning in the OR community.
Advances, Applications & Challenges in Metaheuristics: In Celebration of the Cuckoo Search Algorithm
Guest Editors: Ka-Chun Wong, City University of Hong Kong, Hong Kong (email@example.com) & Thomas Hanne, University of Applied Sciences and Arts Northwestern Switzerland, Switzerland (firstname.lastname@example.org)
Submission Due Date: March 30, 2020
Cuckoo Search, a nature inspired optimization algorithm, was invented by Xin-She Yang (UK) & Suash Deb (India) 10 years ago. Since then it had been proved to be an important & a very useful tool for addressing myriads of optimization problems, both for industrial applications as well as for theoretical ones. Recently the 1st publication of Cuckoo search, published in the year 2009, reached 5000 citations (Google Scholar). In order to celebrate these milestones, SN Operations Research Forum intends to publish a special topical collection by assembling a collection of papers and showcasing the recent efforts in the areas of metaheuristics and nature inspired computing. Accordingly, the journal invites manuscripts, which neither had been published already nor submitted for consideration elsewhere, for this special issue highlighting “Advances, Applications & Challenges in Metaheuristics” to be guest edited by Ka-Chun Wong (Hong Kong) & Thomas Hanne (Switzerland) & due for publication in 2021.
As a result of the significant disruption that is being caused by the COVID-19 pandemic we are very aware that many researchers will have difficulty in meeting the timelines associated with our peer review process during normal times. Please do let us know if you need additional time. Our systems will continue to remind you of the original timelines but we intend to be highly flexible at this time.
About this journal
- Electronic ISSN
- Abstracted and indexed in
- EBSCO Discovery Service
- Google Scholar
- Institute of Scientific and Technical Information of China
- Japanese Science and Technology Agency (JST)
- OCLC WorldCat Discovery Service
- ProQuest-ExLibris Primo
- ProQuest-ExLibris Summon
- Research Papers in Economics (RePEc)
- TD Net Discovery Service