Parallel genetic algorithms for the earliness-tardiness job scheduling problem with general penalty weights

Abstract The purpose of this paper is to develop parallel genetic algorithms for a job scheduling problem on a single machine. The objective of the scheduling is to minimize the total generally weighted earliness and tardiness penalties from a common due date. A binary representation scheme is employed for coding job schedules into chromosomes. Parallel subpopulations are constructed by considering only jobs that can be processed first in the schedule. Three important genetic algorithm operators; reproduction, crossover and mutation are implemented by reflecting the problem-specific properties. The efficiency of the parallel genetic algorithm is illustrated with computational results.

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