International Conference on Operations Research, Statistics and Industrial Engineering

This track focuses on the application of statistical methods to solve complex problems in industrial engineering. Topics may include quality control, reliability analysis, and process optimization.

This session explores the role of data analytics in enhancing operations management practices. Emphasis will be placed on predictive modeling, big data applications, and decision-making frameworks.

This track examines various optimization techniques applicable to engineering problems. Participants are encouraged to present research on linear programming, integer programming, and heuristic methods.

This session delves into statistical quality control methodologies used in manufacturing and service industries. Topics include control charts, process capability analysis, and Six Sigma methodologies.

This track highlights the intersection of industrial engineering and operations research. Contributions may include case studies, theoretical advancements, and innovative applications in various sectors.

This session focuses on the use of simulation modeling to analyze and improve industrial systems. Researchers are invited to present their findings on discrete-event simulation, Monte Carlo methods, and system dynamics.

This track addresses the role of statistical inference in decision-making processes within industrial contexts. Topics may include hypothesis testing, confidence intervals, and Bayesian approaches.

This session explores analytical methods for optimizing supply chain operations. Discussions will cover inventory management, logistics optimization, and demand forecasting techniques.

This track investigates the integration of machine learning techniques in engineering disciplines. Participants are encouraged to share insights on algorithm development, model validation, and real-world applications.

This session focuses on the development and application of statistical models to understand and improve industrial processes. Topics may include regression analysis, time series forecasting, and multivariate analysis.

This track examines methodologies for risk analysis and management in engineering projects. Contributions may include risk assessment frameworks, uncertainty quantification, and mitigation strategies.