Affiliations: DIBRIS, Università degli Studi di Genova, Via Opera Pia, Genoa, Italy
Correspondence:
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Corresponding author: Stefano Demarchi, DIBRIS, Università degli Studi di Genova, Via Opera Pia, 13, 16145 Genoa, Italy. Tel.: +39 010 335 2811; E-mail: [email protected].
Note: [1] Leopoldo Annunziata is with Studio Progettazione Annunziata, Via Giorgio Chiesa, 21/2, 16147 Genoa, Italy. [email protected] — Giuseppe Cicala,Stefano Demarchi, Marco Menapace, Armando Tacchella are with DIBRIS, Università degli Studi di Genova, Via Opera Pia, 13, 16145 Genoa, Italy. [email protected], [email protected], [email protected], [email protected]
Abstract: Like other custom-built machinery, elevators are charecterized by a design process which includes selection, sizing and placement of components to fit a given configuration, satisfy users’ requirements and adhere to stringent normative regulations. Unlike mass-produced items, the design process needs to be repeated almost from scratch each time a new configuration is considered. Since elevators are still designed mostly manually, project engineers must engage in time-consuming and error-prone activities over and over again, leaving little to be reused from one design to the next. Computer automated design can provide a cost-effective solution as it relieves the project engineer from such burdens. However, it introduces new challenges both in terms of efficiency — the search space for solutions grows exponentially in the number of component choices — and effectiveness — the perceived quality of the final design may not be as good as in the manual process. In this paper we compare three mainstream AI techniques that can provide problem-solving capabilities inside our tool LiftCreate for automated elevator design, namely Genetic Algorithms (GAs), Constraint Programming (CP) and Satisfiability Modulo Theories (SMT). A special-purpose heuristic search technique embedded in LiftCreate provides us with a yardstick to evaluate the solutions obtained with GAs, CP and SMT and to assess their feasibility for practical applications.
