This paper describes a Hong Kong MTR Corporation subway project to enhance and extend the current Web-based Engineering Works and Traffic Information Management System (ETMS) with an intelligent “AI Engine.” The challenge is to be able to fully and accurately encapsulate all the necessary domain and operation knowledge on subway engineering works and to be able to apply this knowledge in an efficient manner for both validation as well as scheduling. Since engineering works can only be performed a few hours each night, it is crucially important that the “AI Engine” maximizes the number of jobs done while ensuring operational safety and resource availability. Previously, all constraint/resource checking and scheduling decisions were made manually. The new AI approach streamlines the entire planning, scheduling and rescheduling process and extends the ETMS with intelligent abilities to (1) automatically detect potential conflicts as work requests are entered, (2) check all approved work schedules for any conflicts before execution, (3) generate weekly operational schedules, (4) repair schedules after changes and (5) generate quarterly schedules for planning. The AI Engine uses a rule representation combined with heuristic search and a genetic algorithm for scheduling. An iterative repair algorithm was used for dynamic rescheduling.