Aims & Scope
Aims
Operations Modelling and Analytics (OMA) is an international, peer-reviewed journal dedicated to advancing research and practice in operations research, mathematical modelling, analytics, quality engineering, and risk assessment to support effective decision-making. The journal publishes high-quality original research that develops, applies, or advances quantitative methods, computational techniques, and analytical frameworks for addressing complex problems in management, engineering, and operational systems.
OMA aims to foster methodological innovation and interdisciplinary research by bridging theoretical developments with practical applications. The journal welcomes contributions that enhance the understanding, modelling, optimization, evaluation, and management of complex systems under conditions of uncertainty, with particular emphasis on rigorous analytical methods and their contribution to evidence-based decision-making.
Scope
The scope of Operations Modelling and Analytics encompasses established and emerging methodologies in operations research, modelling, analytics, and decision science, together with their applications across industrial, managerial, engineering, and interdisciplinary contexts. The journal welcomes original contributions in, but not limited to, the following areas:
- Operations Research, Optimization, and Simulation
- Mathematical modelling and optimization
- Linear, nonlinear, integer, and multi-objective programming
- Stochastic modelling, stochastic processes, and simulation
- Queueing theory, inventory control, and scheduling
- Network optimization, routing, logistics, and resource allocation
- Data Envelopment Analysis (DEA) and efficiency measurement
- Modelling and optimization of manufacturing, service, and operational systems
- Discrete-event simulation, agent-based modelling, and system dynamics
- Uncertainty and scenario-based modelling
- Data Analytics and Decision Support
- Descriptive, diagnostic, predictive, and prescriptive analytics
- Statistical modelling, data mining, and machine learning
- Big data analytics and data-driven decision-making
- Predictive modelling and forecasting
- Decision support systems and analytical tools for operational planning
- Integration of analytical methods with optimization and simulation
- Artificial Intelligence and Computational Intelligence
- Artificial intelligence applications in operations modelling and decision-making
- Neural networks and deep learning
- Fuzzy logic and fuzzy inference systems
- Evolutionary algorithms and metaheuristic optimization
- Hybrid intelligent systems and computational intelligence
- AI-based optimization, forecasting, and decision support
- Intelligent methods for uncertainty and risk assessment
- Quality Engineering and Reliability
- Statistical process control (SPC) and process monitoring
- Design of experiments (DOE) and experimental optimization
- Quality engineering, measurement, assurance, and improvement
- Reliability engineering and survival analysis
- Total Quality Management (TQM), Six Sigma, and lean methodologies
- Quality and performance modelling in manufacturing and service systems
- Reliability, quality, and risk assessment in supply chains
- Risk Analysis and Management
- Theoretical foundations and methodologies of risk assessment
- Quantitative and qualitative risk analysis
- Uncertainty modelling and probabilistic risk assessment
- Scenario analysis, sensitivity analysis, and uncertainty quantification
- Operational risk assessment and management
- Risk mitigation, resilience, and business continuity planning
- Integration of risk analysis into optimization and operational decisions
- Decision Analysis and Multi-Criteria Decision-Making
- Decision-making frameworks, models, and strategies
- Multi-criteria decision-making (MCDM) and multi-criteria decision analysis (MCDA)
- Decision-making under uncertainty and risk
- Multi-objective decision analysis and preference modelling
- Fuzzy and hybrid approaches to decision-making
- Integration of optimization, analytics, and risk assessment in decision processes
- Decision support for complex managerial, engineering, and operational problems
- Applications and Interdisciplinary Research
- Supply chain management, logistics, and operational planning
- Healthcare operations, medical decision-making, and patient safety
- Epidemiological modelling and healthcare resource allocation
- Environmental risk assessment and management
- Climate change adaptation and sustainable resource management
- Industrial systems, service operations, and business process improvement
- Resilience and sustainability in operational systems
- Interdisciplinary and participatory approaches to complex operational, risk-related, and decision-making challenges
The journal particularly encourages research that combines methodological rigour with practical relevance, advances existing analytical approaches, or introduces innovative methods for solving complex problems in operations, management, engineering, and related fields. Applications of artificial intelligence, analytics, and computational methods are especially welcome when they offer clear methodological contributions or meaningful advances in modelling, optimization, risk assessment, or decision-making.