The Scheduling Strategies on Ramp Activity Coordination Expert System

초록

Scheduling is an important technology encompassing a wide application area. Due to the complex interrelations among the resources, knowledge, and various other constraints, scheduling has many difficulties. AI technology have been applied to solve the scheduling problem. Science AI techniques are efficient in representing knowledge and dealing with heuristics, it is an adequate methodology to model and to solve scheduling problems. We have implemented the ramp scheduling system, called RACES(Ramp Activity Coordination Expert System), to solve the complex and dynamic aircraft parking problems. RACES has modeled suitably and 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 subproblems and experimental heuristics in the knowledge acquisition process. The system independently processes scheduling for the divided subproblems and shares variables and domains. During the scheduling, the system selects or confines the search space with domain filtering technique by exploiting the characteristics of various constraints and knowledge. RACES produces user driven optimal solution by means of a trade-off scheduling method using heuristics between the size of aircraft and the best-fit time. For 400 to 500 daily flights, RACES made parking schedules of aircraft in about 10 seconds compared with 4 to 5 hours by human experts.