Configuration and design of complex products represents a challenge
in many application fields. The designer must take into account many
different aspects and make decisions typically driven by experience while
taking into account performance constraints and costs. Methods and tools
for design automation represents a viable solution to such complex decision
problems, giving also the possibility to optimize the performance of the final
product on particular context-driven aspects. Artificial intelligence (AI)
algorithms can help in dealing with complexity and enhance the current
tools by supplying solutions in feasible time.
My research is concerned with the development and testing of different
artificial intelligence (AI) techniques to automate the design of elevators.
Elevator design is a problem with many interesting aspects like the need to
deal with a hybrid search state space (continuous and discrete variables)
constrained by design requirements and safety regulations. The study,
design and integration of AI techniques in this particular application field
can provide the end user with design automation tools that output feasible
solutions within acceptable computation times.
My research considered AI techniques such as special-purpose heuristic
search, genetic algorithms and constraint satisfaction to solve elevator
configuration problems. I tested them considering different setups and
parts of the whole design process. I have also implemented a tool L IFT C REATE ,
available as a web application. L IFT C REATE leverages the findings of
my research to automate the design of elevators and, to the best of my
knowledge, there is currently no similar tool publicly available from either
academia or industry that provides the same level of design automation