A parallel genetic-neuro scheduler for job-shop scheduling systems

Article Abstract:

Four main problems need to be addressed with regards to schedule generation in shop floor environments. First, job shop scheduling is classified as an NP-hard problem. Second, scheduling is a problem that is different from shop to shop. Third, other decisions which are not isolated from other functions have an influence on scheduling decisions. Fourth, multiple schedule objectives are usually common in scheduling problems before they can be optimized.

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Genetic neuro-scheduler: a new approach for job shop scheduling

Article Abstract:

Genetic algorithm (GA) and artificial neural networks (ANN) are integrated in the development of a job shop scheduling model. GA is used as an effective search method through gene strings that represent alternative feasible schedules, while ANN provides multiobjective schedule evaluation. The model was better than heuristic algorithms in developing robust and effective results for scheduling problems.

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The "Orchard" scheduler for manufacturing systems

Article Abstract:

A study was conducted to evaluate a heuristic algorithm for scheduling management in manufacturing systems. The algorithm, called 'Orchard,' is assigned the task of scheduling job loading and is designed for specific printed circuit board lines. Results show that the Orchard is flexible enough to be applied to other manufacturing systems which allow operations using multiple machines.

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