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Automated design of systems may require modeling and simulating potential solutions in order to search for feasible ones. This process often involves a trade-off between heuristics and computer-intensive approaches. Since neither of the two methods guarantees to always succeed, each problem domain requires a dedicated evaluation. In this paper, the domain of computer-automated design (CautoD) for elevator systems is studied with the goal of providing experimental evidence about which approach is best in which circumstances, and to serve as guidance for automated modeling of elevator systems

System configuration and design is a well-established topic in AI. While many successful applications exists, there are still areas of manufacturing where AI techniques find little or no application. We focus on one such area, namely building and installation of elevator systems, for which we are developing an automated design and configuration tool. The questions that we address in this paper are: (i) What are the best ways to encode some subtasks of elevator design into constraint-based representations? (ii) What are the best tools available to solve the encodings? We contribute an empirical analysis to address these questions in our domain of interest, as well as the complete set of benchmarks to foster further research.

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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.