Ginger Ke, Memorial University of Newfoundland

Yong Lin, University of Birmingham

Peter Shi, Macquarie University

Feng Tao, Soochow University

Safe handling of hazardous chemicals presents a complex operational challenge across production and logistics systems, requiring integrated decision-making across scheduling, storage, facility location, transport, and recovery processes. Current academic research remains fragmented, addressing single functions in isolation rather than the coordinated system-wide approach needed to meet regulatory requirements and operational realities.

This special issue seeks original research that develops rigorous decision models for hazardous-chemical operations, integrating optimization, simulation, and data-driven methods with practical domain knowledge. The work should bridge operations research, production engineering, logistics management, and safety science to advance both theoretical foundations and practical capabilities for safer, more efficient and sustainable hazardous-chemical operations.

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

Potential topics

• Integrated Production Planning and Hazardous Chemical Management under Safety Constraints
• Robust Storage and Inventory Control for Hazardous Chemicals under Uncertainty
• Intelligent Optimization of Hazardous Chemical Logistics Networks and Transportation Scheduling
• Road Transport Routing and Distribution Optimization for Hazardous Materials with Dynamic Risk
• Safe Disposal and Resource Utilization of Hazardous Chemical Waste in Circular Systems
• Multi-Objective Decision Analytics Balancing Efficiency, Safety, and Sustainability
• Data-Driven Risk Prediction and Real-Time Coordination in Hazardous Chemical Supply Chains
• Emergency Response and Disruption Recovery in Hazardous Chemicals Management Systems
• Game-Theoretic and Incentive Mechanisms for Multi-Stakeholder Hazardous Material Operations
• Policy-Regulated Decision Models for Safe and Circular Hazardous Chemical Management

Timeline

• December 1, 2026: Open for Submissions
• October 31, 2027: 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