Journal of Mathematics Research
Solving Single Machine Scheduling Problem with Maximum Lateness Using a Genetic Algorithm
Abstract
We develop an optimised crossover operator designed by an
undirected bipartite graph within a genetic algorithm for solving
a single machine family scheduling problem, where jobs are
partitioned into families and setup time is required between these
families. The objective is to find a schedule which minimises the
maximum lateness of the jobs in the presence of the sequence
independent family setup times. The results showed that the
proposed algorithm is generating better quality solutions compared
to other variants of genetic algorithms.
undirected bipartite graph within a genetic algorithm for solving
a single machine family scheduling problem, where jobs are
partitioned into families and setup time is required between these
families. The objective is to find a schedule which minimises the
maximum lateness of the jobs in the presence of the sequence
independent family setup times. The results showed that the
proposed algorithm is generating better quality solutions compared
to other variants of genetic algorithms.
This work is licensed under a Creative Commons Attribution 3.0 License.
Journal of Mathematics Research ISSN 1916-9795 (Print) ISSN 1916-9809 (Online)
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