Genetic algorithm-based oil pipeline continuous production scheduling and manufacturing execution system

In the traditional production process of oil pipelines, there are challenges such as complex production workflows, difficulties in progress tracking, and the reliance on manual labor for continuous production scheduling, which are hard to overcome. This paper proposes a comprehensive solution from the perspective of a oil pipeline production management system, aiming to achieve full-process coverage and address continuous production scheduling issues using genetic algorithms. The study presents an intelligent scheduling model based on real-world production challenges. By leveraging genetic algorithms to optimize task allocation for parallel production across multiple units, the approach tackles the key pain point of inefficient manual scheduling. A modular system architecture is constructed, integrating core functional modules such as sales, planning, and procurement, while supporting quality management and traceability to achieve refined management. Experimental results based on genetic algorithms demonstrate that the proposed method effectively resolves the current issues.