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, New York University

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. 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.

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). Still, the focus remained on well-defined, structured problems and a relatively stable industrial context.

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. 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.

Digital twin–driven planning enables real-time updates of plans and schedules, requiring adaptive and rolling-horizon optimization. Hybrid production systems, combining conventional and additive manufacturing, pose new challenges in sequencing, capacity allocation, and cross-technology coordination. Reconfigurable and modular manufacturing requires planning models that adapt to changing system topologies, dynamic routing, and flexible resource reassignments. Circular and sustainable production systems demand that environmental and social objectives be embedded in planning alongside economic goals. 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.

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 and deep learning models are increasingly applied to dynamic planning and scheduling, such as graph neural network and RL architectures for scheduling problems. Hybrid metaheuristics combine classical optimization with machine learning models, surrogate functions, or decomposition techniques to tackle high-dimensional problems. Simulation–optimization coupling allows planners to test, validate, and adapt decisions under complex system dynamics, particularly in systems where analytical modeling is intractable. Multi-agent and distributed approaches enable decentralized planning in highly connected production networks, where local agents coordinate planning decisions. Online and rolling-horizon algorithms become critical when plans must be revised frequently, and incremental updating is required.

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.

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.

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

Bernie F. Quiroga, West Virginia University

Xuying Zhao, Texas A&M University

Oleg Gusikhin, Ford Motor Company

Andrea Matta, Politecnico di Milano

Yong Yin, Doshisha Business School

Professor Kathryn E. Stecke is an internationally renowned scholar whose pioneering work has profoundly shaped the fields of flexible manufacturing systems (FMSs), supply chain management, and production planning. Prof. Stecke has dedicated her career to advancing research on manufacturing efficiencies, supply chain resilience, and innovative production systems, while also bridging the academic discussion between operations and marketing. She is the founding Editor-in-Chief of the International Journal of Flexible Manufacturing Systems and Operations Management Education Review. Her contributions have earned her prestigious recognitions, including being elected a Fellow of the Institute for Operations Research and the Management Sciences (INFORMS) in 2009, of the Production and Operations Management Society (POMS) in 2017, of the Decision Sciences Institute (DSI) in 2019, and of the Asia-Pacific Artificial Intelligence Association (AAIA). She has also been the recipient of the 2013 WORMS Award for the advancement of women in operations research, and the 2008 George E. Kimball medal for recognition of her service to the profession of operations research and the management sciences, and was recognized also by her alma mater, Purdue University, with the Distinguished Woman Scholar Award (2014) and the Outstanding Industrial Engineer Award (2017).

Prof. Stecke's early work laid foundational methodologies for FMS, addressing critical challenges in machine grouping, loading, and production ratios to optimize utilization and flexibility. Stecke (1983), a seminal paper, developed nonlinear integer programming models and hierarchical solution approaches for part type selection, machine grouping, and resource allocation in FMS, providing a framework that has influenced subsequent optimization techniques in automated manufacturing. This article was recognized by INFORMS as one of 50 papers that represented the most significant research published in Management Science in the last half century in February 2004. Berrada and Stecke (1986) introduced algorithmic solutions for balancing workloads in FMS, influencing subsequent research on system efficiency. Stecke and Solberg (1981) explored routing and sequencing strategies to enhance throughput in dynamic manufacturing environments.

Over the decades, her research evolved to encompass supply chain disruptions and adaptive strategies. In Schmitt et al. (2017), winner of the Best Paper Award given by Omega in 2018, she examined resilient ordering policies to counter volatility, providing practical insights for multi-tier networks. Stecke and Kumar (2009) has been the most cited paper in the Journal of Marketing Channels for over a decade. More recently, Prof. Stecke has contributed to understanding innovative production paradigms like the Seru system, a cellular manufacturing approach originating from Japan that emphasizes reconfigurability and responsiveness. Her collaborations with Yin et al. (2017), winner of the Jack Meredith Best Paper Award given by the Journal of Operations Management in 2018, Yin et al. (2018), given a Best Paper Award by IJPR in 2019, and Li et al. (2025) demonstrate how Seru improves rapid response capabilities compared to traditional systems, such as the Toyota Production System, highlighting its potential for flexible manufacturing.

Prof. Stecke has also made significant contributions at the interface between production research and marketing, exploring how operational decisions interact with consumer behavior and market dynamics. Notable works include Zhao et al. (2016), which analyzes conditions under which advance selling strategies enhance profitability across supply chain partners; Zhao et al. (2012) examined optimal quotation modes for customizing lead times and prices; Prasad et al. (2011) investigated advance selling tactics for retailers facing demand uncertainty; Zhao and Stecke (2010) incorporated behavioral economics into pre-order strategies; and Stecke and Zhao (2017) integrated production and logistics for commit-to-delivery models.

This special issue celebrates Prof. Stecke's 75th birthday by inviting contributions that build upon, extend, or reflect on her legacy in production research. It aims to curate cutting-edge articles in active and emerging areas, serving as a focal point for discourse on flexible, resilient, and efficient production systems, as well as the intersection of production research with marketing. By honoring her trailblazing efforts, the issue seeks to inspire future directions in manufacturing and supply chain innovation, ensuring her influence continues to guide the field.

We welcome high-quality submissions, including empirical studies, analytical models, simulation-based research, case analyses, and conceptual papers that offer novel insights. All manuscripts should align with the journal's emphasis on rigorous, impactful research and incorporate key elements such as an exhaustive literature analysis, novel decision-aid models explained for a broad audience, comparisons with state-of-the-art methods, discussions on real-life applications, managerial insights, and research perspectives.

Potential topics

• Advances in flexible manufacturing systems (FMSs), including machine grouping, loading policies, and production ratio optimization for enhanced utilization
• Interfaces between production and marketing, including focal points such as advance selling strategies, pre-order mechanisms considering consumer behavior (e.g., loss aversion), lead time and price quotation modes, production-transportation integration in make-to-order systems, and competition/coordination in online marketplaces
• Reconfigurable production systems, such as Seru, for improving responsiveness and adaptability in volatile environments
• Supply chain resilience strategies, including adaptive ordering, disruption mitigation, and multi-echelon network design amid geopolitical and operational uncertainties
• Production planning and scheduling in capital-intensive systems with machine flexibility, focusing on hierarchical approaches and real-time control
• Integration of digital technologies (e.g., AI, digital twins, and data analytics) in manufacturing and supply chains to boost efficiency and cost control
• Empirical analyses of manufacturing efficiencies in sectors like semiconductors, automotive, and electronics, drawing on case studies of disruption responses
• Comparative studies of traditional vs. innovative production methods, such as Toyota Production System vs. Seru, with emphasis on performance metrics
• Managerial insights for decision-makers on implementing flexible routing, robot scheduling, and state-dependent sequencing in production environments
• Future perspectives on sustainable manufacturing, addressing global supply chain vulnerabilities and policy implications

Timeline

• March 1, 2026: Open for submissions
• October 31, 2026: Submission deadline