An Improved GA for Job-Shop Scheduling Problem

Abstract:

This paper introduces an improved genetic algorithm (GA) to solve the job-shop scheduling problem. A typical case is illustrated to report on how it uses the encoding method to address the problem and to work out the optimal schedules. This method has been tested with ten groups of samples generated randomly. The results show its outperformance to the traditional GA on solving the same problem in terms of best, worst and average ability.

• Periodical: Advanced Materials Research (Volume 630)
• Pages: 486-489
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.630.486
• Online since: December 2012
• Authors: Jian Hong Li
• Keywords: Boundary, Genetic Algorithm (GA), Job-Shop Scheduling, Search Space, Velocity

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