To cope with the increasing complexity of the production process, production planning and control systems have evolved over many years from the original material requirement planning system to today’s advanced planning systems. In such a planning system, managing the complexity is always a pertinent issue, which impacts the overall robustness and adaptivity of the output. The complexity may reside in the production system or result from characteristics or events outside the system. The former is technological complexity, which is related to the inherent complexity of the system and its technologies for both products and systems. The latter is environmental complexity, which describes the coordination between the system and related industries or customers, e.g., raw material supplier and retailer.

Environmental complexity involves a huge amount of uncertain information, which poses a great challenge to the production systems. Multi-criteria Decision Making (MCDM) techniques have been utilized to improve the performance of the complex production system and thereby increase economic growth. While MCDM techniques have already made a significant contribution to complex production systems, it seems the uncertain environment is significantly different from other complexity issues in terms of their dynamic nature, global scale and criticality. How do technological complexity and environmental complexity influence each other? Are there any ways to mitigate the uncertainty? Do we have any comprehensive plan to predict the production system performance under uncertain information by using machine learning-based MCDM? How can models help us minimize economic impacts resulting from the complexity?

This Special Issue aims to publish rigorous research based on the application of advanced MCDM techniques to complex production systems under uncertain information. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

• Advanced MCDM approaches for real-world production planning problems
• Efficient computational methods for solving new mathematical models under uncertainty
• Emergent MCDM techniques for modeling the uncertain factors
• Identification of interactive relationships between technological complexity and environmental complexity
• Modeling dynamics of production systems based on discrete MCDM
• Production system performance prediction using machine learning techniques
• Production system optimization modeling using machine learning-based MCDM