Generative AI in Operations Research: Modeling, Applications and Challenges

Dear Colleagues,

The intersection between Generative Artificial Intelligence (GAI) and Operations Research (OR) presents a fascinating and rapidly evolving area of research with significant potential to revolutionize decision-making processes across various domains. This Special Issue aims to bring together researchers, practitioners, and experts to explore the latest developments, applications, and challenges in leveraging GAI techniques for modeling and optimizing complex operational scenarios. This Special Issue seeks to cover a wide spectrum of topics at the nexus of GAI and OR. It will provide a platform for researchers to present their innovative work, methodologies, and insights related to the following areas:

1. Generative models for operational scenarios: (a). Developments and advancements in various generative models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based models for generating operational data and scenarios. (b). Exploration of techniques for generating realistic data distributions and incorporating domain-specific constraints.
2. Applications of Generative AI in Operations Research: (a). Case studies showcasing successful applications of generative AI in optimization, resource allocation, scheduling, supply chain management, and other relevant OR domains. (b). Examples of how generative models can enhance decision-making processes, risk assessment, and performance improvement.
3. Hybrid approaches and integration: (a). Investigations into combining Generative AI techniques with traditional OR methodologies, such as metaheuristics, simulation, and linear/integer programming, to improve optimization accuracy and efficiency. (b). Demonstration of hybrid approaches that leverage the strengths of both Generative AI and OR to solve real-world problems.
4. Challenges and future directions: (a). Discussion of the challenges and limitations in applying Generative AI to OR, including issues related to data quality, model interpretability, scalability, and ethical considerations. (b). Exploration of future research directions and potential solutions to address the identified challenges.

Dr. Konstantinos Demertzis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• generative models
• operations research
• operations management
• decision-making
• real-time decision support