In a destination control system, a passenger enters his/her desired destination floor via some input device and is then allocated to a specific elevator in the group of elevators. Since the destination is known in advance, new opportunities to optimize the travel routes of elevators become available. Additionally, individual service requirements for each passenger can be defined:
The control software that I developed combines various AI techniques as the basis of a highly optimized and scalable search algorithm that is able to search and prune search spaces of up to $10^{10}$ states. In addition, the algorithm can handle the constraints described above. The AI-based controller was embedded into a service-oriented architecture that coordinates terminals dispatching calls with a group of elevators using agent and auction technology. The developed solution permitted the commercial breakthrough of destination control systems in the elevator industry and became the general foundation for many Schindler elevator products because of its performance, robustness, easy configuration, and general applicability to all variants of elevator control systems. See also the the official product home page by Schinder. The control system is a huge commercial success today that is significantly changing the design of elevators in buildings and that has been adopted by all major competitors in the elevator industry after debating for years that such control systems are infeasible. The system was also awarded a Breaking Barriers Award of the European Commission for its better service to disabled passengers when compared to conventional control systems.
Abstract: The synthesis of elevator control commands is a difficult problem when new service requirements such as VIP service, access restrictions, nonstop travel etc. have to be individually tailored to each passenger. AI planning technology offers a very elegant and flexible solution because the possible actions of a control system can be made explicit and their preconditions and effects can be specified using expressive representation formalisms. Based on the specification, a planner can flexibly synthesize the required control and changes in the specification do not require any reimplementation of the control software. In this paper, we describe the application and investigate how currently available domain-independent planning formalisms can cope with it.
Abstract: Offering an individually tailored service to passengers while maintaining a high transportation capacity of an elevator group is an upcoming challenge in the elevator business, which cannot be met by software methods traditionally used in this industry. AI planning offers a novel solution to these control problems: (1) by synthesizing the optimal control for any situation occurring in a building based on fast search algorithms, (2) by implementing a domain model, which allows to easily add new features to the control software. By embedding the planner into a multi-agent system, real-time interleaved planning and execution is implemented and results in a high-performing, self-adaptive, and modular control software.
Abstract: Not widely known by the AI community, elevator control has become a major field of application for AI technologies. Techniques such as neural networks, genetic algorithms, fuzzy rules, and, recently, multi-agent systems and AI planning have been adopted by leading elevator companies not only to improve the transportation capacity of conventional elevator systems, but also to revolutionize the way in which elevators interact with and serve passengers. In this article, we begin with an overview of AI techniques adopted by this industry and explain the motivations behind the continuous interest in AI. We review and summarize publications that are not easily accessible from the common AI sources. In the second part, we present in more detail a recent development project to apply AI planning and multi-agent systems to elevator control problems.
A model of this domain in PDDL was used in the final round of the International Planning Competition in 2000. Schindler donated awards for the best performing planning systems in this domain during the competition.