Applying a Hybrid Model to Solve the Job-Shop Scheduling Problem with Preventive Maintenance, Sequence-Dependent Setup Times and Unknown Processing Times

Abstract

Although, many researchers propose to optimize the job-shop scheduling problem using all processing times initially available, to mimic a more real-life environment, in this paper, processing times are unknown at the beginning of the optimization. The job-shop scheduling problem is considered with sequence-dependent setup times and preventive maintenance constraints. Processing times are revealed when products arrive at a machine. Unknown processing times will give a more real-world representation, where exact processing times aren't available. A hybrid model, combining discrete event simulation and optimization is applied to simulate the production process and to solve the job-shop problem. The hybrid model uses optimization by creating new production schedules when a job is processed, and when the product arrives at the next machine. The meta-heuristics of the genetic algorithm, ant colony optimization, and simulated annealing algorithm are used. The results showed significantly better results for the hybrid optimization than random sequencing of jobs.