Abstract
This paper adapts metaheuristic methods to develop Pareto optimal solutions to multi-criteria production scheduling problems. Approach is inspired by enhanced versions of genetic algorithms. 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 multi-criteria open shop problem is solved. Final solutions to each are Pareto optimal. The paper concludes with a statistical comparison of the performance of the basic NSGA to NSGA augmented by elitist selection.
Cite
CITATION STYLE
Bagchi, T. P. (2001). Pareto-optimal solutions for multi-objective production scheduling problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1993, pp. 458–471). Springer Verlag. https://doi.org/10.1007/3-540-44719-9_32
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