Techniques for solving constraint satisfiability problems have received much attention in artificial intelligence, operation research and symbolic logic. Applications which may be viewed as CSPs are found in scene identification in computer vision, space and motion planning, database consistency, combinatorial optimization, and cryptarithm puzzle solving. Our research effort attempts to determine which algorithms perform best, and under what conditions, in solving a special CSP known as the student scheduling problem (SSP). Since constraint satisfiability problems are, in general, NP-complete, it is of interest to develop and compare the effectiveness and efficiency of heuristic algorithms as applied, in particular, to our application. In this paper, we assign priorities to the constraints and investigate optimization algorithms for finding schedules which rank high with respect to the priorities. Experimental results have been collected and are reported here. Our system was developed for and used at Bar-Ilan University during the registration period, being available for students to construct their timetables.
ASJC Scopus subject areas
- Computer Science (all)