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Multi-objective Optimization of Flexible Flow-Shop Intelligent Scheduling Based on a Hybrid Intelligent Algorithm

In: Artificial Intelligence for Smart Manufacturing

Author

Listed:
  • Huanhuan Zhang

    (South China University of Technology)

  • Zhenglei He

    (South China University of Technology)

  • Yi Man

    (South China University of Technology)

  • Jigeng Li

    (South China University of Technology)

  • Mengna Hong

    (South China University of Technology)

  • Kim Phuc Tran

    (University of Lille, ENSAIT, ULR 2461 - GEMTEX - Génie et Matériaux Textiles)

Abstract

With the complexity of the production process, the mass quantification of production jobs, and the diversification of production scenarios, research on scheduling problems are bound to develop in a direction closer to the actual production problems. Considering the combination of workshop scheduling problems and process planning problems, the study of such problems is of great significance for improving the production efficiency of enterprises. Therefore, this chapter studies the intelligent scheduling problem of a flexible flow-shop and establishes a two-stage flexible flow-shop scheduling model. On this basis, the fast non-dominated sorting genetic algorithm II (NSGA-II) and the variable neighborhood search algorithm (VNS) are combined to optimize the established two-stage intelligent scheduling model. Finally, a papermaking production process is taken as an example to comprehensively evaluate the performance of the model and the hybrid intelligent algorithm. The experimental results show that the model and algorithm can effectively solve the presented problem.

Suggested Citation

  • Huanhuan Zhang & Zhenglei He & Yi Man & Jigeng Li & Mengna Hong & Kim Phuc Tran, 2023. "Multi-objective Optimization of Flexible Flow-Shop Intelligent Scheduling Based on a Hybrid Intelligent Algorithm," Springer Series in Reliability Engineering, in: Kim Phuc Tran (ed.), Artificial Intelligence for Smart Manufacturing, pages 97-117, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-031-30510-8_6
    DOI: 10.1007/978-3-031-30510-8_6
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