An interactive train scheduling workbench based on artificial intelligence

We describe a practical train scheduling system (called TSS) designed for the Taiwan Railway Bureau (TRB). TSS has two major components. The first is Auto Scheduler which includes an initial scheduler, a conflict finder, and a conflict resolver. Auto Scheduler uses the concept of bugging as problem solving tactics. The second is Manual Scheduler which provides some effective editing functions for users to tune schedules generated by Auto Scheduler. Through an easy-to-use user interface, these components can be used to solve large-scale complex train scheduling problems.<<ETX>>

[1]  Earl D. Sacerdoti,et al.  Problem Solving Tactics , 1979, AI Mag..

[2]  K. Matsumoto,et al.  A knowledge-based interactive train scheduling system-aiming at large-scale complex planning expert systems , 1988, Proceedings of the International Workshop on Artificial Intelligence for Industrial Applications.

[3]  Gerald L. Thompson,et al.  A mixed-initiative scheduling workbench integrating AI, OR and HCI , 1993, Decis. Support Syst..

[4]  Stephen F. Smith,et al.  Issues in the Design of AI-Based Schedulers - Workshop Report , 1991, AI Mag..

[5]  T. Fukuda,et al.  A knowledge-based approach for railway scheduling , 1991, [1991] Proceedings. The Seventh IEEE Conference on Artificial Intelligence Application.

[6]  Toshiharu Hasegawa,et al.  Fundamental algorithm for train scheduling based on artificial intelligence , 1987, Systems and Computers in Japan.

[7]  V. V. S. Sarma,et al.  Knowledge-Based Approaches to Scheduling Problems: A Survey , 1991, IEEE Trans. Knowl. Data Eng..

[8]  John McDermott,et al.  VT: an expert elevator designer that uses knowledge-based backtracking , 1992 .

[9]  John P. McDermott,et al.  VT: An Expert Elevator Designer That Uses Knowledge-Based Backtracking , 1988, AI Mag..