an ai-base approach to destination control in elevators

1. An AI-base Approach to Destination Control in Elevators Jana Koehler & Daniel Ottiger Schindler Lifts Ltd. Presented by Jon Beckham

3. Normal Operation Two to eight cars Usually up and down call buttons Passengers usually don�t know location of cars If not picked up immediately, usually press call button again. And again. Once on, passengers sometimes change their minds and request a different floor Passengers interact with each other, but are not aware Holding elevator doors is courtesy to one passenger, but not to the rest of the people trying to use the elevator. Ideal for passengers is short waiting time and short journey time

4. Criteria for Elevator Control Evaluation HC5% Specifies the handling capacity of a group of cars within five minutes in terms of percentage of building population served. Times Average and maximum journey and waiting times. Humans care more about waiting time than journey time. Getting on an elevator in under a minute is more important than spending three minutes on the elevator.

5. Open Questions What is the objective function for a group dispatching algorithm? Usually a vague combination of waiting and journey times. How can a control system acquire additional information about passenger needs? How can the performance of a controller be improved? How can passenger interfaces be improved beyond the simple buttons?

6. Acquisition of Traffic Information Manual surveys Manually analyzed videotapes Counting system connected to call buttons Weight-sensing devices Computer vision technology

7. Traffic Patterns Up-peak Enter at lobby floor and request upwards transportation Down-peak Request downwards transportation to lobby from any floor Interfloor Request transportation from any floor to any other floor

8. The 80s Expert Systems! Given predefined patterns, passenger counts, rules from human lift experts, the system would transition into the hand-crafted optimal dispatching mode for the situation. Didn�t work because� Identifying the patterns didn�t work. Human lift experts are busy people, they don�t have time to update the rules all the time.

9. The Early 90s How about Fuzzy Logic? The traffic intensity was defined fuzzily, then fuzzy rules determined the predominant traffic pattern which in turn influenced elevator control. Didn�t work because predicting traffic flow proved impossible.

10. The mid-90s Neural networks! Train on simulations, identify one out of five predefined traffic patterns. Same as expert systems in the 80s, didn�t overcome problems of pattern-triggered rules.

11. A Little Bit Later� OTIS�s fancier Neural Network Dispatching decision based on estimated remaining response time (RRT) Trained in simulation, attempts to minimize RRT errors Improved predictions up to 20% on average Simple perceptron isn�t adequate representation of RRT

12. Yet Another Approach Neural Nets in a Reinforcement Learning framework Objective function: minimize squared sum of waiting times 60,000 hours of training only on down-peak patterns Outputs of STOP or CONTINUE-DOWN Compared to comically simple policies, and not surprisingly proved to be better Building specific training makes this method pretty useless

13. Last One! Genetic Algorithms Fitness function: weighted sum of waiting time, journey time, estimated passenger load per car �The usefulness of genetic algorithms is not yet clear. Finding the right combination of specific crossover, mutation and selection methods yielding good dispatching decisions poses a challenge in this domain.�

14. Ha, that wasn�t the last one. Combinatorial Optimization of Travel Routes Minimin (single player minimax) lookahead search with alpha pruning One floor decision executed, re-search, repeat

15. Destination Control Systems Miconic-10� was introduced by Schindler in 1996. 1500 elevators have been equipped with Miconic-10�. Doubles HC5% of conventional dispatching algorithms. Yay! Ten digit keypad instead of traditional up/down call buttons. Allows for better prediction of travel routes, thus better utilization of lifts.

16. The Future of Elevators Access restrictions Oskar lives in the penthouse, doesn�t want the factory workers to be able to go to his floor. VIP service Firefighters/medical personnel need to be able to immediately get control of elevators. Separation of passenger groups In hotels, don�t want food service and housekeepers on the same lift for hygienic reasons.

17. Finally, the purpose of the paper The algorithmic methods used in the elevator industry have thus far not allowed for those functionalities to be integrated directly into the normal operation of a group of elevators.

18. Destination Control NP-hard The allocation problem with destination calls can be defined as follows: Given a number n of destination calls with boarding floor b and exit floor e we wish to compute a totally ordered sequence of stops S such that each s corresponds to a given boarding or exit floor and where each b precedes the respective e.

19. Model Destination is� Planning Problem? Scheduling Problem? Constraint Satisfaction Problem?

20. This is AI Planning So it�s most naturally a planning problem. Initial state is described by the current distribution of passengers and elevators. Goal state is any state satisfying that all passengers have been delivered to their destination floors. Available actions: STOP, UP, DOWN, OPEN, CLOSE

21. Subproblems Static, offline optimization for one elevator Dynamic, online allocation problem for several cars Algorithm should allow new services to be added to destination control. Should be able to handle multi-deck elevators.

22. Physical System

23. Offline Problem The size of the search space is determined by stops, not by passenger volume. Real-time requirements are demanding. Must be able to compute instant allocations of passengers to cars. Must be able to quickly recompute travel routes to handle traffic changes. Upper time bound of 100ms.

24. The Algorithm Depth-first branch-and-bound modified for forward-checking from constraint reasoning. Allows for faster pruning of states in violation of service requirements. Computes optimal stop sequences for lifts with arbitrary numbers of decks. Prunes about 2/3 of states. Scales up past the biggest lift system.

25. Effectiveness of Pruning

26. Online Problem Auctioning methods used. Each elevator can communicate via asynchronous messaging supporting publish/subscribe mechanisms and allowing p2p communication between lift components. New agents can dynamically register. (Ad hoc networking)

27. Auction Method

28. Contract Net Protocol Broker � receives offer requests from terminals, adds requests to the world model. Car driver � responsible for executing the plans. Observer � continuously updates the world model of the planner. Failure recovery � monitors plan execution, diagnoses problems, initiates recovery actions. Drive � executes start and stop commands. Doors � execute opening commands. Configuration manager � provides information about building layout.

29. Job Manager as Holon

30. Empirical Results

31. Problems With higher traffic, plans are replaced often, thus long plans unlikely to be executed. Solution, restrict plan length. Why use comprehensive plans? Why bother with figuring out optimal plans for everyone in the building instead of just the next optimal step? Often taking everything into account provides the means for an in-depth analysis of traffic.

32. Available Improvements Communication between passengers and elevators could be improved significantly. Current elevator systems constitute a significant waste of space. (One shaft per car.)