Generative AI in Operations Research: Modeling, Applications and Challenges
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 30 November 2024 | Viewed by 1067
Special Issue Editor
Interests: model-agnostic meta-learning; multi-task learning; real-time analytics; scalable and compassable privacy-preserving data mining; automated assessment and response systems; AI anomaly detection; AI malware analysis; AI IDS-IPS; AI forensics; AI in blockchain
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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:
- 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.
- 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.
- 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.
- 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
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Keywords
- generative models
- operations research
- operations management
- decision-making
- real-time decision support
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