The re-entrant flow shop scheduling problem considering time windows constraint is one of the most important problems in hard-disc drive (HDD) manufacturing systems. In order to maximise the system throughput, the problem of minimising the makespan with zero loss is considered. In this paper, evolutionary techniques are proposed to solve the complex re-entrant scheduling problem with time windows constraint in manufacturing HDD devices with lot size. This problem can be formulated as a deterministic Fm | fmls, rcrc, temp | C max problem. A hybrid genetic algorithm was used for constructing chromosomes by checking and repairing time window constraints, and improving chromosomes by a left-shift heuristic as a local search algorithm. An adaptive hybrid genetic algorithm was eventually developed to solve this problem by using fuzzy logic control in order to enhance the search ability of the genetic algorithm. Finally, numerical experiments were carried out to demonstrate the efficiency of the developed approaches.

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This paper focused on optimal scheduling solutions for the reentrant flow shop (RFS) with uniform machines and different product families in order to minimize the makespan and mean flow time in the production of hard-drive components. An RFS environment includes several workstations, each of which consists of only one machine. At each stage, the machine can process any job. Each job must be produced according to the reentrant flow. This problem can be denoted as Fm | fmls, recrc | C max , ∑ f j /n. Although the throughput maximization can be given by minimizing the maximum completion time, it does not ensure minimizing the length of the time interval between the release time and completion time of all jobs. For production quality, each job should be produced in a short flow time, since some processes must be controlled by the aging condition. Thus, the approach of a single criterion, such as minimizing the makespan, is not sufficient for handling the problem; bi-objective optimization should be considered instead. For optimal convergence, meta-heuristics, such as simulated annealing or a genetic algorithm, should be studied for practical use as a support tool for scheduling jobs. This paper applied a multi-objective genetic algorithm (MOGA) for solving the RFS problem. The results showed that MOGA could optimally solve the problem with reasonable computational effort.

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