Group elevator scheduling has long been recognized as an important problem for building transportation efficiency, since unsatisfactory elevator service is one of the major complaints of building tenants. It now has a new significance driven by homeland security concerns. The problem, however, is difficult because of complicated elevator dynamics, uncertain traffic in various patterns, and the combinatorial nature of discrete optimization. With the advent of technologies, one important trend is

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... to use advance information collected from devices such as destination entry, radio frequency identification, and sensor networks to reduce uncertainties and improve efficiency. How to effectively utilize such information remains an open and challenging issue. This paper presents the optimized scheduling of a group of elevators with destination entry and future traffic information for normal operations and coordinated emergency evacuation. Key problem characteristics are abstracted to establish a two-level separable formulation. A decomposition and coordination approach is then developed, where subproblems are solved by ordinal optimization-based local search, and top ranked nodes are selectively optimized by using dynamic programming. The approach is then extended to handle up-peak with little or no future traffic information, elevator parking for low intensity traffic, and coordinated emergency evacuation. Numerical testing results demonstrate near-optimal solution quality, computational efficiency, the value of future traffic information, and the potential of using elevators for emergency evacuation. Note to Practitioners-This paper studies group elevator scheduling with destination entry and future traffic information for normal operations, as well as for coordinated emergency evacuation. By exploiting the separable problem structure, a two-level formulation is established capable of modeling advance information. An approach is then developed by incorporating several innovative ideas into a decomposition and coordination framework, aiming to achieve near-optimal performance. The approach has also been extended for cases with little or no future traffic information and coordinated emergency evacuation. Numerical testing results are encouraging and further improvement is needed to reduce CPU time for online implementation.