International Journal of Production Research

Innovations in Production Planning: Emerging Problems and Modern Solution Paradigms

Production planning has historically been a central pillar of industrial engineering and operations management. Since the mid-20th century, the field has developed a rich body of classical problems and models, such as lot sizing, job shop and flow shop scheduling, capacity planning, assembly line balancing, material requirements planning (MRP), and aggregate production planning (Pinedo, 2016; Battaïa and Dolgui, 2013; Battaïa and Dolgui, 2022; Boysen et al., 2022). These models provided structured ways to allocate resources, balance supply and demand, and coordinate activities across production systems, often under deterministic assumptions and with objectives focused primarily on cost and efficiency. Linear programming, dynamic programming, and combinatorial optimization offered rigorous formulations and exact methods, while heuristics and metaheuristics emerged to address large-scale and computationally complex instances (e.g., Aspern et al., 2025; Hermelin et al., 2025; Kucukkoc et al., 2023).

Over time, production planning research progressively expanded to address uncertainty (via stochastic and robust optimization), multi-objective trade-offs (e.g. cost, service level, inventory, lead times), and integration across planning levels (strategic, tactical, and operational) (Lee and Moon, 2025; Huang et al., 2024; Wang et al., 2026). Still, the focus remained on well-defined, structured problems and a relatively stable industrial context (e.g., Lee and Moon, 2024).

Today, however, technological, environmental, and societal shifts are reshaping the landscape of production planning. The rise of cyber-physical systems, IoT-enabled factories, advanced robotics, digital twins, additive manufacturing, and AI-based automation has disrupted the stability of traditional assumptions (Yilmaz et al., 2026; Cakici et al., 2024; Kucukkoc et al., 2025; Battaïa et al., 2025). These innovations have created new types of planning problems, characterized by more frequent reconfiguration, increased heterogeneity of resources, and the necessity of integrating multiple technologies and objectives. For instance:

• Digital twin–driven planning enables real-time updates of plans and schedules, requiring adaptive and rolling-horizon optimization (e.g., Rodriguez and Rossit, 2025; Dolgui et al., 2025; Dolgui and Ivanov, 2021).
• Hybrid production systems, combining conventional and additive manufacturing, pose new challenges in sequencing, capacity allocation, and cross-technology coordination (e.g., Cantini et al., 2022; Kucukkoc et al., 2025; Cakici et al., 2025; Lolli et al., 2025).
• Reconfigurable and modular manufacturing requires planning models that adapt to changing system topologies, dynamic routing, and flexible resource reassignments (e.g., Battaïa et al., 2025).
• Circular and sustainable production systems demand that environmental and social objectives be embedded in planning alongside economic goals (e.g., Peron et al., 2025; Kucukkoc, 2024).
• Resilience-oriented planning has become critical in the face of global disruptions, pandemics, and supply shocks, demanding strategies that balance efficiency with adaptability and robustness (e.g., Lee and Moon, 2025; Dolgui and Ivanov, 2021).

At the same time, innovation has not only transformed the problems but also the solution approaches. Whereas traditional production planning was dominated by mathematical programming and rule-based heuristics, the current era witnesses the rapid adoption of data-driven, hybrid, and AI-enabled methods:

• Reinforcement learning / deep learning models are increasingly applied to dynamic planning and scheduling, e.g., graph neural network and RL architectures for scheduling problems (e.g., Lei et al., 2023; Woo and Moon, 2026; Elyasi et al., 2026; Wang et al., 2026).
• Hybrid metaheuristics combine classical optimization with machine learning models, surrogate functions, or decomposition techniques to tackle high-dimensional problems (e.g., Khachai et al., 2025; Hermelin et al., 2025; Aspern et al., 2025; Beldar et al, 2025).
• Simulation–optimization coupling allows planners to test, validate, and adapt decisions under complex system dynamics, particularly in systems where analytical modeling is intractable (e.g., Huang et al., 2024; Lolli et al., 2024; Cantini et al., 2025).
• Multi-agent and distributed approaches enable decentralized planning in highly connected production networks, where local agents coordinate planning decisions (e.g., Yuraszeck et al., 2025).
• Online / rolling-horizon algorithms become critical when plans must be revised frequently, and incremental updating is required (e.g., Park and Moon, 2025; Woo and Moon, 2025).

Despite these advances, major challenges persist. Industrial adoption demands bridging the gap between academic models and real-world constraints: data sparsity, computational scalability, integration with legacy systems (ERP/MES), and user trust. Decision-makers must also reconcile conflicting objectives: efficiency vs. resilience, speed vs. sustainability, optimality vs. interpretability.
Against this backdrop, this special issue seeks to collect innovative contributions that redefine the boundaries of production planning. We welcome papers that introduce novel problem formulations reflecting new industrial realities and innovative solution approaches leveraging modern computational, AI, and optimization tools. Both theoretical/methodological and empirical/applied submissions are encouraged, including case studies and empirical validation.

Topics may include (but are not limited to):

• 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)

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