The objective of this thesis is to explore the application of artificial intelligence
(AI) techniques for modelling in flexible manufacturing. The work consists of three
main parts. In the first part, the structure and performance of various types of flexibly
automated batch manufacturing systems are discussed, the modelling challenge for the
design of these types of manufacturing systems is identified, and the currently available
modelling techniques are examined and comparatively assessed.
In the second part, the research into the structure and design of a knowledge
based modelling system is reported. Potential advantages of AI techniques for
manufacturing systems modelling are identified. The modelling system is then
developed using the LOOPS knowledge engineering language on the Xerox 1186 AI
Workstation. Major features of the modelling system include its knowledge driven
requirement to enab,l e evaluation of alternative systems with different criteria, the
capability of modelling over multiple levels of detail, the transparency of its solution
procedure, and the modularity of the system structure to allow convenient modification
The third part is concerned with the evaluation of the AI based modelling method ..
Parallel experiments are conducted on an extended case study cell by using the
knowledge based modelling system, the emulator and the tool flow modelling system.
Merits of the AI based method are then critically assessed, drawn on the comparison of
the results obtained from the three studies. Conclusions drawn from this research and
directions for future work are finally indicated.
A Doctoral Thesis submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University of Technology.