Central to the manufacturing goal of meeting the conflicting requirements of short production lead times and efficient operation is the issue of appropriate control architectures for automated manufacturing systems (AMS):​ i.e., what decision-making structures result in a modifiable, reliable, and fault-tolerant system? Experience has shown that traditional, centralised architectures can be quite inflexible to change and provide little fault tolerance. This has led industrial and academic researchers to the development of a spectrum of decentralised control architectures. At one end of the spectrum lie the hierarchical control architectures with theoretical foundations in organisational theory and large-scale system control theory. At the other end lie non-hierarchical, or "heterarchical", structures arising from more recent developments in artificial intelligence, distributed computing, and object-oriented programming.

Existing industrial and academic research on AMS control has focused on qualitative comparisons of alternative structures and has done little more than prove the concept of heterarchical control. This dissertation aims to address this issue by:​ (i) identifying key parameters of the manufacturing control problem that can be used to characterise alternative control architectures, and (ii) objectively evaluating the relative performance of various control architectures. It is intended that, in the future, this work could be used to determine whether specific control architectures are appropriate for solving given manufacturing system control problems.