HEURISTIC APPROACHES TO SOLVING MULTIOBJECTIVE SCHEDULING OPTIMISATION PROBLEMS

Abstract

In recent years, different heuristic and hyperheuristic optimization approaches involving natural computing techniques have been suggested to solve multiobjective combinatorial optimization problems. These techniques have been applied — individually and in hybrid forms — with relative success to scheduling problems. This work attempts to review the performance of the three common techniques often applied in scheduling problems: Genetic Algorithms (GA), Simulated Annealing (SA) and Tabu Search (TS).