Using genetic algorithms for parallel machine multi-objective scheduling problems with families and one resource constraint

Multi-objective scheduling on parallel machines with families and one resource constraint is considered. The objectives are total tardiness and maximum make-span. This paper proposes genetic algorithms using tournament selection, niche count, and violation degree to solve this problem. The computational results from random orders show that the genetic algorithms are more efficient than rectified Earliest Due Date, Longest Processing Time and Shortest Processing Time in solving the problem.