Pareto optimal production scheduling by meta-heuristic methods

Summary form only given as follows. This paper proposes metaheuristic methods to develop Pareto optimal solutions to multi-criteria production scheduling problems. The approach is inspired by enhanced versions of genetic algorithms. The method first extends the nondominated sorting genetic algorithm (NSGA), a method recently proposed to produce Pareto-optimal solutions to numerical multi-objective problems. Multi-criteria flowshop scheduling is addressed next. Multi-criteria job shop scheduling is subsequently examined. Lastly the multicriteria open shop problem is solved. Final solutions to each are presented as 2D or 3D Pareto optimal displays. The paper concludes with a statistical comparison of the performance of the basic NSGA to NSGA augmented by elitist selection.