Group elevator scheduling has received considerable attention due to its importance to transportation efficiency for mid-rise and high-rise buildings. One important trend to improve elevator systems is to collect advance traffic information. Nevertheless, it remains a challenge to develop new scheduling methods which can effectively utilize such information. This paper is to solve the group elevator scheduling problem with advance traffic information. This problem is difficult due to various traffic patterns, complicated car dynamics, and combinatorial explosion of the search space. A two-level formulation is developed with passenger-to-car assignment at the high-level and single car dispatching that is innovatively formulated as passenger-to-trip assignment at the low-level. Detailed car dynamics are embedded in simulation models for performance evaluation. Taking advantage of advance information, a new door action control method is suggested to increase the flexibility of elevators. In view of the hierarchical problem structure, a two-level optimization framework is established. Key problem characteristics are exploited to develop an effective trip-based heuristic for single car dispatching, and a hybrid nested partitions and genetic algorithm method for passenger-to-car assignment which can be extended to solve a generic class of sequential decision problems. Numerical results demonstrate solution quality, computational efficiency, benefit of advance information and the new door action control method, and values of new features in our hybrid method.

Group elevator scheduling is important for transportation efficiency in mid-rise and high-rise buildings, and incessant efforts have been made to improve the service efficiency of elevators. Although these efforts have achieved performance improvements, the performance limit remains an open issue. This paper tries to address that goal by estimating the optimal performance of group elevator scheduling with complete knowledge of future traffic information. A two-level minimization formulation is presented, with passenger-to-car at the high level, and single car dispatching at the low level. The low level is formulated as a passenger-to-trip assignment problem by using a concept trip to facilitate the description of single car dispatching strategies. In view of the difficulty to obtain the absolute optimal performance, our goal turns into its upper and lower bounds. The upper bound is obtained by finding a good feasible solution to this problem. The lower bound is obtained by finding the lower bound for a newly constructed problem whose optimal performance is less than or equal to that of the original problem. Numerical results demonstrate the effectiveness and the scalability of our method