This special issue invites research on the application of multicriteria decision aiding methods to address complex challenges in sustainable energy and environmental planning. The work seeks contributions that demonstrate how decision support techniques can inform policy and strategic planning in these critical areas.

Appel publié par Socio-Economic Planning Sciences. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Timeline

• January 31, 2027: Full paper submission deadline

Yi-Kuei Lin, National Yang Ming Chiao Tung University

This special issue seeks contributions on reliability modeling and decision support for industrial engineering systems, including manufacturing, supply chains, logistics, energy systems, transportation, and service operations. While traditional probabilistic and stochastic approaches remain important, advances in sensor technologies and data collection have enabled new data-driven approaches that combine machine learning and statistical methods with classical reliability theory.

The special issue welcomes both theoretical developments and practical applications that demonstrate how data-driven reliability analysis can enhance decision-making in maintenance planning, resource allocation, system design, and operational optimization. Papers should show methodological rigor and clear relevance to real-world industrial challenges, illustrating how reliability-informed approaches improve system performance.

Appel publié par Annals of Operations Research. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Data-driven reliability modeling and parameter estimation using operational data
• Reliability analysis using statistical learning and machine learning methods
• Reliability models combining analytical formulations with data-driven learning approaches
• Decision support systems incorporating reliability analysis and risk information
• Optimization and decision-making problems informed by reliability performance
• Predictive maintenance and condition monitoring using data and learning models
• Multistate system and network reliability analysis
• Reliability evaluation of manufacturing, logistics, and supply chain systems
• Reliability modeling in energy, power, and infrastructure systems
• Stochastic modeling and uncertainty analysis in data-driven reliability studies
• Simulation, enumeration, and approximation methods for reliability evaluation
• System design and resource allocation considering reliability and operational data
• Reliability analysis supported by digital twins, data analytics, and intelligent systems
• Reliability, robustness, and resilience of industrial engineering systems

Timeline

• July 31, 2027: Submission deadline

Dries F. Benoit, Ghent University

Kristof Coussement, IÉSEG School of Management

Cem İyigün, Middle East Technical University

Asil Oztekin, University of Massachusetts Lowell

This special section seeks to publish research that bridges data science and operations research, emphasizing both theoretical advances and practical organizational applications. The focus extends beyond technical innovation to address how analytics creates measurable value within organizations and drives necessary organizational change.

The collection invites contributions exploring the intersection of OR and analytics across multiple dimensions: ethical and governance considerations in data usage, challenges of applying OR techniques to big data and distributed systems, organizational barriers to analytics adoption, data quality and validation methods for large datasets, and the role of visualization and soft OR techniques in supporting data-driven decision making.

Appel publié par Annals of Operations Research. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Ethics and governance issues in analytics: How should data be obtained? What are the ethical implications of using applications of analytics to influence behavior?
• Big data and analytics: What are the limitations and applications of optimization and other OR techniques to large datasets? What are the challenges for applications of OR methods within distributed systems? What is the possibility that OR models could in fact be the producers of big data, e.g., large-scale simulation models? What new methods/models in response to big data, e.g., sentiment mining, can be adopted by OR?
• Organizational issues in analytics adoption: What are the issues facing organizations trying to adopt analytics? What is the role of real-time applications of OR in organizations?
• Data quality and analytics: What methods can be used for hypothesis testing and model validation in large datasets? How can unstructured data be used effectively in OR models? What is the role of multi-methodology in business analytics? What opportunities do open data present for the OR discipline?
• Analytics and decision support: How can data visualization techniques be used across the breadth of OR? What role do problem structuring and "soft" OR techniques play in analytics and big data projects?

Timeline

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