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Algorithm for parallel machines scheduling problem to minimize the earliness and tardiness costs is proposed in this study. The problem is associated with the assignment of jobs to machines and determination of staring time for each job in a given sequence. Population-based incremental learning (PBIL) algorithm is used to allocate the jobs to machines. The optimal timing algorithm based on the minimum block cost function calculation is then employed to decide the starting time of jobs on each machine. To illustrate the performance of proposed algorithm, numerical examples generated randomly are tested. The numerical results obtained from PBIL combined with optimal timing algorithm called PBILOTA are compared to EDDPM (Earliest Due Date for Parallel Machines) to indicate the decrease in penalty cost. From the experimental results, it is shown that PBILOTA is an efficient algorithm for solving parallel machines scheduling problem with earliness-tardiness costs minimization.
Keywords: population-based incremental learning algorithm; scheduling; parallel machines; earliness; tardiness
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