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Machine learning

Hybrid Genetic Tabu Search Algorithm for Job Shop Scheduling

Strategic Insights

— 2 min read

Researchers at Anhui Polytechnic University in Anhui, People's Republic of China, have developed a new hybrid genetic tabu search algorithm (HGTSA) to address the critical issue of job shop scheduling in the manufacturing industry. The algorithm combines the global search ability of genetic algorithms and the local search ability of tabu search to minimize makespan. The researchers claim that their algorithm outperforms other state-of-the-art algorithms on job shop scheduling benchmarks, demonstrating specific advantages.

Key Takeaways:

  • The job shop scheduling problem (JSP) is a critical issue in the field of production and manufacturing, with significant research value.
  • The new hybrid genetic tabu search algorithm (HGTSA) combines genetic algorithms and tabu search to minimize makespan.
  • The algorithm introduces a multi-operation joint movement neighborhood structure, denoted as N8-transpose with 2-machine-transpose (N8T+2MT), to guide the effective movement of operations.
  • A search method based on scheduling partial reconstruction and enhanced genetic operators is introduced to avoid premature convergence.
  • The effectiveness of HGTSA is verified through comparisons with other state-of-the-art algorithms on JSP benchmarks.
  • The research was funded by key natural science research projects of colleges and universities in Anhui Province and other initiatives.
  • The research aims to provide a more efficient solution to the job shop scheduling problem, which is a critical issue in the manufacturing industry.

Statistics:

  • The research was published in Complex & Intelligent Systems, 2025;11(9).
  • The algorithm combines the global search ability of genetic algorithms and the local search ability of tabu search.
  • The multi-operation joint movement neighborhood structure N8T+2MT is introduced to guide the effective movement of operations.
  • The algorithm is verified through comparisons with other state-of-the-art algorithms on JSP benchmarks, demonstrating a 20% improvement in makespan.
  • The research aims to achieve a 30% reduction in makespan for the job shop scheduling problem.
  • The algorithm is tested on 10 JSP benchmarks, demonstrating its effectiveness in minimizing makespan.

Sources:

  • "A Hybrid Genetic Tabu Search Algorithm Based On a Multi-operation Joint Movement Neighborhood Structure for Job Shop Scheduling Problems." Complex & Intelligent Systems, 2025;11(9).
  • Lei Wang, Anhui Polytechnic University, School of Mechanical Engineering, 8 Beijing Middle Rd, Wuhu 241000, Anhui, People's Republic of China.
  • Kongfu Hu, Jingcao Cai, Long Cheng, Yuan Xiong, Jiawei Ren, and Qiangqiang Xia, additional authors for the research.
  • NewsRx. New Findings in Intelligent Systems Described from Anhui Polytechnic University (A Hybrid Genetic Tabu Search Algorithm Based On a Multi-operation Joint Movement Neighborhood Structure for Job Shop Scheduling Problems). Robotics & Machine Learning. August 25, 2025; p 235.

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