An Application of Genetic Algorithms for the Flexible Job-shop Scheduling Problem

The intelligent scheduling system has been proposed in this study and successfully applied to the scheduling of a screw manufacturer located at Taiwan, we aim added a concept of multi-objective optimization into practical scheduling system to promote the efficient of schedule and production. In order to meet user's requirement, an improved genetic algorithm (GA) with a multi-objective design was adopted as a core technique of this intelligent system. It differs from traditional GA scheduling solution, our model capable of avoided incorrectly assigned a job to infeasible machine; thus, special designs in evolution process of GA are not required. A real case study was introduced in this paper, and the proposed model has been tested with 100 work orders, the empirical results were indicated that the proposed approach outperforms manual scheduling, regardless of the total orders completed time, machine retooling times, and the average load rate of machine.

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An Application of Genetic Algorithms for the Flexible Job-shop Scheduling Problem

Cited by 10 publications

References 13 publications

Towards an Embedded Distributed Implementations of PSO Solutions for the Flexible Job Shop Problem

Towards an Embedded Distributed Implementations of PSO Solutions for the Flexible Job Shop Problem

A novel Hybrid Harris hawk sine cosine optimization algorithm for reactive power optimization problem

Design of an evolutionary multi-objective optimization scheduling system for a screw manufacturer

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