Expert system for scheduling in an airline gate allocation

摘要

Scheduling is an important technique encompassing a wide application area. Because of the complex interrelations among the resources, knowledge, and various other constraints, scheduling has many difficulties. Artificial Intelligence technology has been applied to solve the scheduling problem. As AI techniques are efficient in representing knowledge and dealing with heuristics, it is an adequate approach to model and to solve scheduling problems. We have implemented the ramp scheduling system, called RACES (Ramp Activity Coordination Expert System), to solve complex and dynamic aircraft parking problems. RACES was developed from the domain knowledge and experience which were acquired from the domain experts. Domain knowledge and experience are important factors in controlling the scheduling procedure. RACES divides the problem into sub-problems and experimental heuristics in the knowledge acquisition process. The system independently processes scheduling for the divided sub-problems and shares variables and domains. During the scheduling, the system selects or confines the search space with domain filtering techniques by exploiting the characteristics of various constraints and knowledge. RACES produces a user-driven near-optimal solution by means of a trade-off scheduling method using heuristics between the size of aircraft and the best-fit time. For 400 daily flights, RACES made parking schedules for aircraft in about 20 s compared with 4–5 h by human experts.