Adele Marshall, Queen's University Belfast

Laura Boyle, Queen's University Belfast

Sally Brailsford, University of Southampton

Erwin Hans, University of Twente

Brian Denton, University of Michigan

Martin Kunc, University of Southampton

Healthcare systems globally face mounting challenges from aging populations and workforce constraints while simultaneously benefiting from expanding digital health data and artificial intelligence capabilities. This special issue explores how Operational Research and AI methods can be combined to improve healthcare planning, delivery, and evaluation. Building on prior work examining the OR-AI interface, this collection focuses specifically on healthcare applications where reliable and verifiable AI is critical.

The special issue seeks original research demonstrating substantive contributions at the intersection of OR and AI in healthcare contexts. Submissions should include methodological advances, applied studies with measurable practical value, and work addressing real-world implementation challenges. Papers focused solely on optimization or simulation without significant AI integration may be more appropriate for other venues.

Appel publié par Journal of the Operational Research Society. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Machine learning and predictive modelling for patient flow, demand forecasting, and clinical decision support integrated with OR
• Large language models, generative AI, and retrieval-augmented generation for extracting modelling insight from unstructured clinical documents and health records
• Reinforcement learning and approximate dynamic programming for sequential decisions in screening, treatment, and care planning
• Generative approaches to synthetic health data for modelling and privacy-preserving analysis
• Optimisation of healthcare resources including workforce planning, scheduling, and capacity management using integrated AI and OR methodologies
• Hybrid simulation–AI approaches for the design and evaluation of care delivery
• Data-driven and digital-twin models of care pathways and healthcare delivery
• Statistical and stochastic modelling of patient journeys and survival
• AI-enhanced planning and delivery of care in emergency, unscheduled, and elective settings
• Personalised and stratified care through AI-enabled pathways, screening, prevention, and chronic disease management
• Human–AI collaboration in clinical and operational decision-making
• Trustworthy AI in health addressing equity, ethics, transparency, and verification
• Implementation and evaluation of OR/AI models in healthcare practice

Timeline

• Invalid DateTime: Expected publication
• January 15, 2027: Submission deadline
• April 1, 2027: First-round decisions

Goutte Stephane, Université Paris Saclay

This special issue addresses the fragmentation between environmental sciences, energy systems, public health, and finance by promoting integrated forecasting methodologies capable of anticipating cascading systemic risks. Research communities need to develop common predictive frameworks that capture complex feedback mechanisms linking environmental governance, ecosystem services, financial markets and societal resilience under unprecedented uncertainty.

The special issue welcomes innovations in econometric, statistical, machine learning and artificial intelligence approaches using novel data sources such as satellite imagery and alternative data. Key focus areas include electricity system resilience under renewable integration, carbon market forecasting, natural capital accounting, sustainable finance, and the distributional impacts of energy transitions.

Contributions are sought globally with emphasis on emerging and developing economies perspectives. The collection will comprise 10-12 peer-reviewed articles demonstrating methodological diversity and practical relevance for policymakers, regulators, investors and sustainability managers.

Appel publié par Journal of Forecasting. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Forecasting climate physical risk impacts on asset prices, sovereign credit ratings, and insurance markets
• Climate transition risk scenario modeling and stress testing for banks and institutional investors
• Predicting the financial materiality of climate policy shocks (carbon taxes, border adjustment mechanisms)
• Nowcasting and forecasting climate-related litigation and regulatory enforcement risks
• Volatility forecasting under compound climate extremes and cascading natural hazards
• Predictive models linking air/water pollution exposure to healthcare costs, labor productivity, and insurance claims
• Forecasting the economic and financial spillovers of zoonotic disease outbreaks and pandemics
• Ecosystem degradation leading indicators for public health expenditure forecasting
• Early warning systems for environmental health emergencies using multi-source data fusion
• Forecasting the macroeconomic impact of environmental regulatory tightening in emerging economies
• Carbon credit and emissions allowance price forecasting (spot, futures, and options markets)
• Green bond pricing, yield spread determinants, and default risk prediction
• ESG rating dynamics: forecasting rating changes, rating disagreement, and market impact
• Predicting the performance of climate-themed investment portfolios and low-carbon equity indices
• Forecasting the diffusion of green financial innovations (sustainability-linked loans, transition bonds)
• Forecasting the economic value of biodiversity loss and ecosystem service degradation
• Predictive models for natural capital accounting and sovereign wealth fund risk exposure
• Land-use change and deforestation risk forecasting for supply chain finance and commodity markets
• Water scarcity prediction and its implications for agricultural commodity pricing and sovereign risk
• Forecasting the insurance and reinsurance implications of ecosystem tipping points
• Forecasting the differential impact of climate policies on developed vs. emerging economies
• Predicting capital flow reversals triggered by environmental shocks in frontier markets
• Sovereign debt sustainability forecasting under climate and ecological stress
• Forecasting international trade disruptions from environmental regulations and carbon border adjustments
• Cross-border spillover effects of environmental governance changes: a global forecasting perspective
• Machine learning and deep learning architectures for environmental-financial data
• Graph neural networks for forecasting interconnected climate-financial networks
• Mixed-frequency and nowcasting approaches for environmental indicators and market variables
• Uncertainty quantification and probabilistic forecasting under climate deep uncertainty
• Conformal prediction, Bayesian nonparametrics, and ensemble methods for environmental-financial prediction
• Large language models (LLMs) and alternative data for environmental risk nowcasting
• Forecasting electricity demand, renewable generation and flexibility needs in low-carbon power systems
• Electricity price forecasting under renewable intermittency, extreme weather events and geopolitical disruptions
• Early warning systems for power-system vulnerability, cascading failures and blackout risks
• Forecasting resilience indicators for transmission and distribution networks
• Forecasting flexibility resources including storage, demand response and data centers
• AI-driven forecasting for smart grids and decentralized electricity systems
• Forecasting electricity markets under high renewable penetration and climate uncertainty
• Forecasting critical mineral demand for the energy transition
• Energy poverty, energy justice and vulnerability forecasting
• Multi-energy forecasting integrating electricity, hydrogen and storage systems

Timeline

• September 30, 2027: Submission deadline
• April 30, 2028: Final decisions

Associate editors

• Xiang Gao, Shanghai Business School
• Jingjia Zhang, Nankai University

Sara Wilkinson, University of Technology Sydney

Johnny Wong, University of Technology Sydney

Biyanka Ekanayake, University of Technology Sydney

This special issue examines how artificial intelligence is transforming the built environment across construction, real estate, facilities management, and urban planning. While AI technologies offer significant opportunities for innovation and efficiency through applications like energy optimization, smart building control, and predictive maintenance, the sector faces critical challenges regarding data integrity, algorithmic bias, privacy, and transparent decision-making.

The special issue seeks high-quality original research exploring AI's use, impact, risks, and future potential in the built environment. It welcomes contributions spanning construction automation, property valuation, smart buildings, urban planning, generative design, AI governance and ethics, sustainability applications, security systems, human-AI collaboration, and professional education in AI.

Appel publié par Building Research & Information. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• AI in construction: automation, robotics, quality control, progress monitoring, risk and safety analytics
• AI in property and real estate: valuation models, market intelligence, customer engagement, asset management, predictive analytics
• Smart buildings and facilities management: IoT-enabled optimisation, energy management, predictive maintenance, AI-driven knowledge systems
• AI in urban planning and infrastructure: spatial modelling, forecasting, environmental assessment, transport and mobility analytics
• Design and engineering applications: generative design, simulation, optimisation, digital twins, parametric modelling
• Explainable AI (XAI): AI governance, ethics, and regulation: transparency, bias, data integrity, privacy, and responsible GenAI in the built environment
• AI for sustainability and ESG: carbon modelling, resource optimisation, lifecycle assessment
• Security and risk management: computer vision for surveillance, access control, anomaly detection
• Multimodal and agentic AI: advanced AI systems applied to complex built environment challenges
• Human-AI collaboration: professional adoption, skills, workflows, and organisational change
• The challenge of educating and training built environment professionals in AI

Timeline

• September 30, 2026: Deadline for submission
• December 31, 2026: Deadline for review
• February 28, 2027: Deadline for revised submission
• April 30, 2027: Deadline for approval final manuscript

Yan-Fu Li, Tsinghua University

Hoang Pham, Rutgers University

Xu-Feng Zhao, Nanjing University of Aeronautics and Astronautics

Industrial systems produce vast sensor-generated datasets that strain computational resources. Advanced machine learning techniques including transfer learning, federated learning, quantum machine learning, and reinforcement learning provide scalable solutions for handling large data volumes and enabling informed decision-making. Recent applications of these techniques to system reliability management encompass fault diagnosis, health assessment, remaining useful life prediction, degradation analysis, and maintenance optimization. This special issue seeks original research papers, reviews, and case studies exploring these emerging applications.

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

Potential topics

• Deep learning for anomaly detection and/or fault detection
• Machine learning-enhanced maintenance optimization
• Federated learning for system monitoring and predictive maintenance
• Reinforcement learning for maintenance decision making
• Transfer learning for cross-domain maintenance
• Generative models for synthetic data generation for predictive maintenance
• Self-supervised learning for unlabeled data for fault detection
• Large models for fault detection and/or diagnosis
• Generative models for reliability testing
• Meta-heuristics for large-scale reliability design optimization

Timeline

• September 30, 2026: Submission deadline

Hoang Pham, Rutgers University

The special issue addresses the growing importance of reliability and statistical computing in artificial intelligence systems used in everyday applications and service industries. It seeks contributions covering both theoretical advances and practical implementations in these areas, with emphasis on papers demonstrating real-world applicability.

The special issue welcomes research on mathematical and statistical methods for reliability, machine learning approaches for intelligent systems, big data analysis techniques, and system dependability measures. Industrial applications are particularly valued, including case studies from fields such as robotics, healthcare, education, surveillance, and transportation.

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

Potential topics

• Mathematical reliability and statistical methods
• Big data modeling and prediction
• Statistical learning algorithms, models, and theories
• Machine learning models for intelligent systems
• Text mining and deep machine learning
• Intelligent system dependability and performability
• Reliability modeling and optimization
• High-dimensional data analysis
• Statistical inference for intelligent systems
• Industrial case studies in intelligent systems

Timeline

• October 30, 2026: Submission deadline

Andrea Flori, Politecnico di Milano

Anna Maria Gambaro, Università del Piemonte Orientale

Ioannis Kyriakou, University of London

Duc Khuong Nguyen, EMLV Business School

Climate-related operational risks pose significant threats to financial and economic systems, particularly as economies transition toward low-carbon models. The timing and pace of this transition create substantial uncertainties, especially for carbon-intensive firms that must balance profitability with decarbonization goals. Vulnerabilities extend through interconnected global supply chains where disruptions propagate indirectly, amplifying operational and systemic risks across multiple tiers.

Financial markets increasingly transmit climate-related risks through asset prices, volatility, and liquidity shocks, potentially triggering market instability through supply chain relationships. This special issue addresses the gap in understanding how climate and environmental risks propagate through financial and economic systems, seeking contributions that employ robust stochastic optimization, machine learning, and advanced forecasting methods to inform portfolio allocation and risk management decisions in the face of evolving climate regulations and transition uncertainties.

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

Potential topics

• Robust stochastic optimization methods for climate transition risks
• Portfolio optimization and risk-adjusted return modelling under climate policy uncertainty
• Credit and counterparty risk assessment under low-carbon transition scenarios
• Risk-sharing mechanisms and insurance models for climate-related disruptions
• Supply chain risk management in climate transition
• Operational decision-making in emission trading schemes and carbon pricing
• Predictive modelling of carbon stranding risk via supervised learning
• Machine learning and big data analytics for climate risk scenario classification
• Natural language processing applications in climate policy risk analysis
• Bayesian network approaches to modelling climate transition risk propagation
• Agent-based models of climate transition dynamics and systemic effects

Timeline

• December 31, 2026: Submission deadline

Stelios Bekiros, University of Turin

Andrea D'Ariano, Roma Tre University

Peng Wu, Fuzhou University

Guowei Zhang, Dalian University of Technology

Modern operations research increasingly involves complex decision-making in data-rich environments where contextual information can significantly improve operational policies. This special issue focuses on integrating side information into optimization frameworks, combining advances from optimization, machine learning, and statistical learning. The challenge is to develop methods that go beyond historical averages to create adaptive, personalized solutions that effectively handle high-dimensional data, distribution shifts, and real-time computational demands.

The special issue welcomes contributions that develop novel theories, algorithms, and practical applications merging optimization with machine learning and data analytics. Priority is given to methods that address uncertainty while leveraging contextual information, with emphasis on improving actual decision quality and system performance rather than predictive accuracy alone.

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

Potential topics

• Contextual Stochastic Optimization
• Distributionally Robust Optimization with Side Information
• Prescriptive Analytics and Contextual Decision Making
• Learning-Enhanced Optimization
• Decision-Focused Learning
• Online, Dynamic, and Adaptive Optimization
• Learning-Augmented Algorithms
• Interpretable and Trustworthy Optimization Models
• Statistical Guarantees and Generalization in Optimization
• Data-Driven Optimization Under Uncertainty
• AI and Machine Learning for OR
• Human-in-the-Loop Optimization
• Optimization with Foundation Models or Generative AI
• Scalable Algorithms for Large-Scale Optimization Problems
• Digital Twin and Real-Time Decision Systems
• Contextual Transportation and Logistics Optimization
• Contextual Supply Chain and Revenue Management
• Data-Driven Healthcare and Service Operations

Timeline

• July 31, 2027: 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

Appel publié par Medical Decision Making. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Timeline

• Invalid DateTime: Submission deadline

Mirco Peron, NEOMA Business School

Ibrahim Kucukkoc, Balikesir University

Daniel Alejandro Rossit, Universidad Nacional del Sur

Ilkyeong Moon, Seoul National University

Olga Battaïa, KEDGE Business School

Michael Pinedo, Stern School of Business, New York University

Production planning has long been central to operations management, with classical problems like scheduling and capacity planning addressed through mathematical programming and heuristics. However, emerging technologies—cyber-physical systems, IoT, digital twins, additive manufacturing, and AI—are fundamentally transforming both the problems planners face and the methods available to solve them.

These technological shifts have introduced new planning challenges: digital twin-driven real-time optimization, hybrid conventional-additive manufacturing systems, reconfigurable production topologies, circular economy objectives, and resilience under global disruptions. Concurrently, solution approaches have evolved from traditional optimization to data-driven methods including reinforcement learning, hybrid metaheuristics, simulation-optimization frameworks, and multi-agent systems.

Appel publié par International Journal of Production Research. Lire l'appel complet sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Evolution of classical planning problems (lot sizing, capacity planning, scheduling, assembly line balancing) in modern manufacturing contexts
• Planning in reconfigurable, modular, and hybrid manufacturing systems
• Integration of additive manufacturing and conventional processes in planning
• Additive manufacturing production scheduling
• Digital twin–enabled planning and real-time adaptive scheduling
• Production planning under sustainability, circular economy, emissions or carbon goals
• Resilience-oriented planning under uncertainty, disruptions, and volatility
• Advanced optimization methods: decomposition, robust/stochastic models, metaheuristics
• Machine learning, reinforcement learning, hybrid AI–optimization for planning
• Simulation–optimization frameworks and surrogate modeling
• Production Planning problems associated with customized environments (engineering-to-order, make-to-order, mass customization)
• Human–robot collaborative systems and operator-driven planning in the context of Industry 5.0
• Reinforcement learning / deep learning models applied to dynamic planning and scheduling, (e.g., graph neural network and RL architectures for scheduling problems)
• Optimization of production and inventory strategies in modern distribution systems (e.g., e-commerce, platform-based logistics)

Timeline

• September 30, 2026: Manuscript deadline

Cristina Alaimo, ESSEC Business School

Lauren Waardenburg, ESSEC Business School

Jonny Holmström, Umeå University

Lior Zalmanson, Tel Aviv University

Reza M. Baygi, VU Amsterdam

The special issue addresses a fundamental shift in how artificial intelligence operates: contemporary AI systems are built upon, learn from, and generate data at scale, making data rather than rules the core substrate of computational intelligence. This requires Information Systems scholars to develop new conceptual frameworks for understanding the data-AI nexus as a socio-technical phenomenon, moving beyond purely technical perspectives.

The call emphasizes that data are shaped by institutional practices and cognitive frames, while AI systems increasingly reshape the data they depend on, blurring boundaries between data and AI. Practitioners primarily engage with AI through data practices such as curation, labelling, and quality control, making it essential to understand the organizational routines and historical infrastructures that underpin these processes. Additionally, AI now generates synthetic data that feeds back into model training, raising institutional and epistemic challenges.

Appel publié par European Journal of Information Systems. Lire l'appel complet sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Data and AI co-constitution: exploring where intelligence emerges in computational architectures, datasets, or their entanglement; how data selection, curation, labelling, and historical accumulation shape AI capabilities
• AI as a data-maker: conceptualizing AI as an active producer of data; how classification systems, predictive models, and generative tools reorganize reality; AI agents autonomously generating and acting upon data; recursive dynamics of synthetic data training other AI systems
• Data decoupled from practice: consequences of data production detached from institutionalized knowledge practices; governance and decision-making based on decontextualized data; risks when data-driven systems lack ties to local knowledge and professional judgment
• The social life of the data-AI nexus: institutional, historical, and cognitive trajectories of data; how organizational memory, institutional logics, and collective sensemaking shape datasets and AI output interpretation; AI agents as new organizational actors
• Theoretical and methodological discussions: analyses of co-constitution, entanglement, flow, recursion, performativity, and classification; reconceptualizing constructs like intelligence, data quality, ground truth, and model performance as relational and emergent
• Cross-level insights: how assumptions about data and intelligence are embedded in AI systems; studies of data work (curation, labelling, cleaning), model development, synthetic data pipelines, generative AI integration
• Data, AI and grand challenges: addressing climate change, public health, inequality, and geopolitical tensions; responsible AI design with incomplete, biased, or synthetic data; governance and accountability in distinctive contexts (scientific, environmental, planetary)

Timeline

• May 28, 2026: Call launch at the Theorizing Data & AI Conference
• January 15, 2027: Initial paper submission deadline
• April 15, 2027: First round authors notification
• July 31, 2027: Invited revisions deadline
• October 31, 2027: Second round authors notification
• January 15, 2028: Final revision deadline

Associate editors

• Aleksi Aaltonen,
• Ida Asadi Someh,
• Ioanna Constantiou,
• Domenico di Prisco,
• Mayur Joshi,
• Ekaterina Jussupow,
• Jannis Kallinikos,
• Tomislav Karačić,
• Stan Karanasios,
• Alexander Kempton,
• Angelos Kostis,
• Harris Kyriakou,
• Christine Legner,
• Kalle Lyytinen,
• Eric Monteiro,
• Jeff Parsons,
• Mike Power,
• Jan Recker,
• Paavo Ritala,
• Marta Stelmaszak Rosa,
• Lauri Wesssel,