Aims & Scope

Aims

The Journal of Intelligent Operations and Analytics (JIOA) aims to provide a rigorous, peer-reviewed platform for advancing cutting-edge research, theoretical development, and practical applications of intelligent systems, advanced analytics, and emerging digital technologies across all domains of operations and decision sciences. The journal explores how intelligent operations—enabled by artificial intelligence (AI), machine learning, optimization, data analytics, digital twins, Internet of Things (IoT), and cyber-physical systems—are transforming organizational performance, operational excellence, and strategic decision-making. JIOA focuses on innovations in operations management, supply chain and logistics, manufacturing systems, service operations, healthcare operations, smart cities, sustainability, and Industry 4.0/5.0 environments. It is published quarterly online by Scilight Press.

Scope

JIOA welcomes interdisciplinary contributions that integrate operations research, information systems, data science, and management to address complex real-world problems. The journal also encourages research on the societal, ethical, regulatory, and educational implications of intelligent operations and analytics, including responsible AI, transparency, resilience, and sustainability. Topics of contributions in the journal include, but are not limited to:

  • Optimization models and algorithms for operations and supply chain systems
  • Stochastic modeling, simulation, and uncertainty analysis in operations
  • Multi-criteria decision-making (MCDM) methods in operations management
  • Data-driven decision support systems for operational excellence
  • Prescriptive analytics and optimization for production and service operations
  • Scheduling, routing, and resource allocation models in complex systems
  • Inventory theory, demand forecasting, and capacity planning
  • Supply chain design, coordination, and network optimization
  • Logistics and transportation modeling, including last-mile delivery optimization
  • Resilient and risk-aware supply chain modeling under disruptions and uncertainty
  • Intelligent manufacturing systems, smart production planning, and control
  • Integration of AI and machine learning with operations research methodologies
  • Hybrid modeling approaches combining optimization, simulation, and analytics
  • Digital twins and real-time decision-making in operations systems
  • Queueing theory and service system optimization
  • Facility location, layout design, and network configuration models
  • Sustainable and green operations: optimization for energy, waste, and emissions reduction
  • Circular supply chain modeling and reverse logistics optimization
  • Human–machine collaboration in operations decision-making environments
  • Explainable and interpretable models in operations research
  • Heuristics, metaheuristics, and evolutionary algorithms for large-scale optimization
  • Game theory and strategic decision-making in operations and supply chains
  • Performance measurement, benchmarking, and continuous improvement models
  • Healthcare operations research: patient flow, scheduling, and resource optimization
  • Public sector and humanitarian logistics optimization
  • Operations research applications in Industry 4.0/5.0 environments