Innovations in Production Planning: Emerging Problems and Modern Solution Paradigms

Éditeurs invités

• 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

Synthèse

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.

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Thèmes proposés

• 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)