Abstract: We propose the adaptive algorithm for solving a set of similar scheduling problems using learning technology. It is devised to combine the merits of an exact algorithm based on the mixed graph model and heuristics oriented on the real-world scheduling problems. The former may ensure high quality of the solution by means of an implicit exhausting enumeration of the feasible schedules. The latter may be developed for certain type of problems using their peculiarities. The main idea of the learning technology is to produce effective (in performance measure) and efficient (in computational time) heuristics by adapting local decisions for the scheduling problems under consideration. Adaptation is realized at the stage of learning while solving a set of sample scheduling problems using a branch-and-bound algorithm and structuring knowledge using pattern recognition apparatus.

Keywords: Scheduling, mixed graph, learning, pattern recognition.

ACM Classification Keywords: F.2.2 Nonnumerical algorithms and problems: sequencing and scheduling.

Link:

LEARNING TECHNOLOGY IN SCHEDULING BASED ON THE MIXED GRAPHS

Yuri Sotskov, Nadezhda Sotskova, Leonid Rudoi

http://www.foibg.com/ijita/vol12/ijita12-4-p12.pdf