SIMTech-NTU Joint Lab on Complex Systems
Block N4, B1a-01 | School of Computer Engineering, Nanyang Technological University | Singapore 639798


The research is also in line with which School thrust?
28 January 2015 - 1:49pm

Agent-Based Modeling & Simulations

Agent-based modelling and simulation (ABMS) is one of the core research thrust and focus of SCE that serves as an enabler of the joint lab. ABMS is a relatively new approach to modelling complex systems. An agent-based model is defined by a collection of interacting autonomous agents with individually defined properties and behaviours. Through development of simple rules and temporal interactions of the many agents, more complex macro-level system properties will emerge. In the joint lab, research members working on this thrust are developing automaton-based discrete-event supply chain simulation models that are composed from software components that represent types of supply chain agents (like retailers, manufacturers, transporters), their constituent control elements (like inventory policy), and their interaction protocols (like message types). The underlying library of supply chain modeling components will be derived from analysis of several different supply chains and research made on vehicle routing systems. The aim is to provide a reusable base of domain-specific primitives or memes that enables rapid development of customized decision support tools.

Complex Systems Optimization

Complex Systems Optimization is one of the other core research thrust and focus of SCE that serves as an enabler of the joint lab. In the joint lab, the research members also specialize in the research field of state-of-the-art complex optimization technologies, such as evolutionary and memetic optimization, which specializes on using the notion of meme(s) as units of information encoded in computational representations for the purpose of complex problem-solving. Memetic optimization covers a plethora of potentially rich meme-inspired computing methodologies, frameworks and operational algorithms including simple hybrids, adaptive hybrids and memetic automaton, which has been successfully applied to many real-world application in various problems with dynamic and uncertain environment, multi-criteria design and decision making, various NP-hard continuous and combinatorial problems in fields such as distribution network problems, assignment problems, etc.

Data Analytics, Mining and Computational Intelligence

Data Analytics and Mining is one of the core research thrust and focus of SCE, which serves as a critical enabling technology of the joint lab. In this thrust, research members of the joint lab explores on techniques that automatically extract hidden patterns and potentially useful knowledge from large volumes of data in real-world applications efficiently, effectively and robustly. This is a highly interdisciplinary area that integrates techniques and faculty members of diverse fields, including database systems, statistics, machine learning, and artificial intelligence. Data analytics and mining technologies have been broadly applied to many real-world applications in a wide range of domains, such as marketing and customer relationship management in business, financial industry, e-commerce, medicine and health industry, telecommunications, bioinformatics, software engineering and the IT industry.