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Optimization Model for Job Shop Scheduling Based on Genetic Algorithm

In: Proceedings of 20th International Conference on Industrial Engineering and Engineering Management

Author

Listed:
  • Yu-jie Zhu

    (Northeast Forestry University)

  • Ya-min Liang

    (Northeast Forestry University)

Abstract

JSSP is short for Job-shop Scheduling Problem. With multi-constraint, multi-objective and random uncertainty, JSSP is optimization problems and also the most difficult constraints combinatorial optimization problems. JSSP is an important aspect of modern enterprise manufacturing operations under advanced manufacturing mode. As an effective mean of solving complex scheduling problems in many areas related to scheduling, Genetic Algorithm (GA) has been effectively applied. With random, highly parallel and adaptive intelligent features, GA has showed its unique advantages in solving problems with complex combination of multiple constraints, such as JSSP. This article will establish an optimization model for JSSP based on genetic algorithm and solve the model with MATLAB, and then output the result of the scheduling with a Gantt chart. There will be an example to verify the feasibility and effectiveness of the algorithms and models.

Suggested Citation

  • Yu-jie Zhu & Ya-min Liang, 2013. "Optimization Model for Job Shop Scheduling Based on Genetic Algorithm," Springer Books, in: Ershi Qi & Jiang Shen & Runliang Dou (ed.), Proceedings of 20th International Conference on Industrial Engineering and Engineering Management, edition 127, pages 863-872, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-40063-6_85
    DOI: 10.1007/978-3-642-40063-6_85
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