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Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems

Author

Listed:
  • Muhammad Kamal Amjad
  • Shahid Ikramullah Butt
  • Rubeena Kousar
  • Riaz Ahmad
  • Mujtaba Hassan Agha
  • Zhang Faping
  • Naveed Anjum
  • Umer Asgher

Abstract

Flexible Job Shop Scheduling Problem (FJSSP) is an extension of the classical Job Shop Scheduling Problem (JSSP). The FJSSP is known to be NP-hard problem with regard to optimization and it is very difficult to find reasonably accurate solutions of the problem instances in a rational time. Extensive research has been carried out in this area especially over the span of the last 20 years in which the hybrid approaches involving Genetic Algorithm (GA) have gained the most popularity. Keeping in view this aspect, this article presents a comprehensive literature review of the FJSSPs solved using the GA. The survey is further extended by the inclusion of the hybrid GA (hGA) techniques used in the solution of the problem. This review will give readers an insight into use of certain parameters in their future research along with future research directions.

Suggested Citation

  • Muhammad Kamal Amjad & Shahid Ikramullah Butt & Rubeena Kousar & Riaz Ahmad & Mujtaba Hassan Agha & Zhang Faping & Naveed Anjum & Umer Asgher, 2018. "Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-32, February.
    • Handle: RePEc:hin:jnlmpe:9270802
    DOI: 10.1155/2018/9270802
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    Citations

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    Cited by:

    1. Seyed Mahdi Homayouni & Dalila B. M. M. Fontes, 2021. "Production and transport scheduling in flexible job shop manufacturing systems," Journal of Global Optimization, Springer, vol. 79(2), pages 463-502, February.
    2. Tamssaouet, Karim & Dauzère-Pérès, Stéphane & Knopp, Sebastian & Bitar, Abdoul & Yugma, Claude, 2022. "Multiobjective optimization for complex flexible job-shop scheduling problems," European Journal of Operational Research, Elsevier, vol. 296(1), pages 87-100.
    3. Ming Jiang & Haihan Yu & Jiaqing Chen, 2023. "Improved Self-Learning Genetic Algorithm for Solving Flexible Job Shop Scheduling," Mathematics, MDPI, vol. 11(22), pages 1-17, November.
    4. Yingli Li & Jiahai Wang & Zhengwei Liu, 2022. "A simple two-agent system for multi-objective flexible job-shop scheduling," Journal of Combinatorial Optimization, Springer, vol. 43(1), pages 42-64, January.
    5. Shun Jia & Yang Yang & Shuyu Li & Shang Wang & Anbang Li & Wei Cai & Yang Liu & Jian Hao & Luoke Hu, 2024. "The Green Flexible Job-Shop Scheduling Problem Considering Cost, Carbon Emissions, and Customer Satisfaction under Time-of-Use Electricity Pricing," Sustainability, MDPI, vol. 16(6), pages 1-22, March.
    6. Neumann, Anas & Hajji, Adnene & Rekik, Monia & Pellerin, Robert, 2024. "Integrated planning and scheduling of engineer-to-order projects using a Lamarckian Layered Genetic Algorithm," International Journal of Production Economics, Elsevier, vol. 267(C).

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