Aims & Scope

Computers & Operations Research is a privileged forum for state-of-the-art work exploring the theory and practice of Operations Research (OR) intertwined with advanced computational methodologies. The journal publishes high-quality innovative and impactful research articles in theories, modeling, algorithms, and applications of Operations Research.

Since its first volume was printed in 1974, the journal has promoted different OR application areas that include but are not limited to Transportation, Logistics, Manufacturing, and Supply Chain Management. The journal has also triggered new trends in OR models and techniques such as Data Analytics, Decision-making under Uncertainty, and Behavioral OR. A strong focus has been put on recent advances in Statistics, Data Science, and Machine Learning.

The form, content, and language of the articles should take cognizance of the breadth of applications covered by OR and of the consequent fact that many readers may not be experts in the scientific field to which the computer and operations research techniques are applied by the author. In particular, the abstract should concisely describe:

1. the investigated problem(s),
2. the theoretical or methodological contribution, and
3. the insights.

The journal is structured around seven major areas which, nonetheless, are not disjointed:

    Transportation;
    Logistics and Supply Chains;
    Production and Scheduling;
    Decision-Making under Uncertainty and Data Analytics;
    Optimization—Exact Methods;
    Optimization—Approximate Methods;
    Machine Learning and Data Science. 

The journal also publishes state-of-the-art surveys and best practice guides in Analytics, Operations Research, and Management Science. These articles are printed in a special section, “Surveys in Operations Research and Management Science”. Submissions can focus on theory or applications of OR/MS and can be of several types, including but not limited to:

1. Results that are considered standards by experts in the community but which have not been documented in textbooks;
2. Standard results which have been, in some way, streamlined, such as, for example, new proof techniques leading to more elegant derivations of known results;
3. New developments in methodology or new application areas ('hot topics').

A survey should be critical with respect to the existing knowledge and should focus on computational and algorithmic aspects.

The journal publishes focused issues on topics of interest related to its editorial mission. Such issues typically contain between six and twelve articles. They are put together within an eighteen-month period under the responsibility of one or several guest editors. Prospective guest editors are encouraged to contact the Editor-in-chief.

Fully reproducible results are core for papers accepted for publication in the journal. Hence, authors should grant full access to the data and codes underlying their work. Further information on different possibilities to accomplish this can be found at https://www.sciencedirect.com/journal/computers-and-operations-research/publish/guide-for-authors.

Annual Best Paper Award

Every year, the journal’s editors select one paper to receive the “CAOR Annual Best Paper Award”. The paper will be made free-to-view for one year, thus receiving privileged visibility. The winning and the runner-up papers will also be highlighted on the website in the “Editor’s Choice” section, staying there for one year.

Further Information for authors

It is the responsibility of the authors to ensure that the submitted manuscripts are written using proper English, that possible grammatical or spelling errors are eliminated and that the text conforms to correct scientific English. Submissions that do not satisfy these criteria may be rejected without being sent to reviewers.

All full-length research papers published in the journal must demonstrate constructive algorithmic complexity and extensive numerical experiments. Numerical illustrations (examples) are not sufficient: the numerical experiments must have a scientific value of their own, particularly with comparisons to other approaches. The use of real-world data is also valued.

In CAOR, (meta)Heuristics other than well-established algorithms such as evolutionary algorithms or ant colony optimization must be described in metaphor-free language. This is a way to ensure that they are immediately comparable to existing algorithms.

The Harvard style is adopted in the journal for the bibliography. Authors should consider this while preparing their manuscripts, especially when preparing tables and charts involving bibliography references.

The highly innovative contributions targeted by the journal prevent it from endorsing papers that present long and unsolvable formulations for variants or extensions of existing problems and then resort to a well-established approximate method to find approximate solutions.

CAOR does not endorse papers emerging in the context of warfare activities planning. The authors working in that and related fields should look for specialized journals.

Special Issue on Stochastic Simulation and AI: Advancing the Frontiers of Decision-Making
提出日: 2026-01-31

Guest editors:

    L. Jeff Hong, University of Minnesota, USA
    Guangwu Liu, City University of Hong Kong, China
    Jun Luo, Shanghai Jiao Tong University, China (Managing Guest Editor)

Special issue information:

Stochastic simulation and artificial intelligence (AI) are two powerful methodologies that have pushed the boundaries of modern decision-making and optimization. Stochastic simulation, long recognized as a critical tool for analyzing complex and uncertain systems, has been widely applied across fields such as manufacturing, healthcare, supply chain, and finance. Meanwhile, AI, with its rapid advancements in machine learning and data-driven techniques, has revolutionized how systems are modeled, optimized, and understood. The convergence of these two methodologies has opened new frontiers in research and application, enabling more efficient, intelligent, and adaptive solutions to real-world challenges.

The synergy between these fields manifests in multiple ways. AI techniques can enhance simulation by enabling more sophisticated modeling approaches, improving computational efficiency, and providing better optimization strategies. Meanwhile, simulation serves as a crucial tool for AI development by generating synthetic training data, providing environments for reinforcement learning, and enabling systematic evaluation of AI models under various scenarios.

This special issue, "Stochastic Simulation and AI: Advancing the Frontiers of Decision-Making" in Computers & Operations Research, seeks to highlight the latest advancements, methodologies, and applications at the intersection of these two powerful fields. Topics of interest for this special issue include, but are not limited to:

    Advanced AI techniques/algorithms for accelerating simulation computations
    Simulation optimization in AI-driven decision-making
    Simulation methodologies for fine-tuning and validating AI models
    Derivative-free simulation optimization methods with AI integration
    Quantifying input uncertainty in complex simulation or AI models
    Applications in transportation, logistics, manufacturing, supply chains, healthcare, finance, etc.

Note that all papers submitted to the special issue must comply with the aims and scope of the journal (https://www.sciencedirect.com/journal/computers-and-operations-research).

Manuscript submission information:

    Submission open: May 15, 2025.
    Submission deadline: January 31, 2026
    Final decision: September 30, 2026

Authors are advised to select the article type of 'VSI: Stochastic Simulation and AI' to make sure the submissions are grouped under this special issue.

Keywords:

stochastic simulation, artificial intelligence, decision-making, computational efficiency, applications