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<p>In car production, the sequencing buffer area often fails to align the exit sequence of the paint workshop with the entry sequence required by assembly-workshop’s constraints. The sequence difference significantly decreases car production efficiency and substantially raises production costs. Existing works often simplify optimization objectives and constraints, making them unable to be transferred to real-world car production. In this paper, we establish a multi-constraint optimization model for the Multi-objective Painted Body Storage (MPBS) problem, and propose a RTM Scheduling Mechanism enhanced Improved Genetic (RSM-IG) algorithm to solve the MPBS problem. Specifically, we first design a car-receiving transverse scheduling mechanism based on a greedy strategy, and then integrate it into a genetic algorithm to solve the MPBS problem. Extensive experiments on two real-world datasets show that our method surpasses the state-of-the-art (SOTA) by an average of 23.6% and 41.8% respectively in the comprehensive score of four optimization objectives.</p>

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An improved genetic algorithm for multi-objective painted body storage scheduling problem

  • 期刊论文

  • 发表日期：

    2026年1月17日

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  • Di Zhang,
  • Lijuan Wang,
  • Na Li

摘要

In car production, the sequencing buffer area often fails to align the exit sequence of the paint workshop with the entry sequence required by assembly-workshop’s constraints. The sequence difference significantly decreases car production efficiency and substantially raises production costs. Existing works often simplify optimization objectives and constraints, making them unable to be transferred to real-world car production. In this paper, we establish a multi-constraint optimization model for the Multi-objective Painted Body Storage (MPBS) problem, and propose a RTM Scheduling Mechanism enhanced Improved Genetic (RSM-IG) algorithm to solve the MPBS problem. Specifically, we first design a car-receiving transverse scheduling mechanism based on a greedy strategy, and then integrate it into a genetic algorithm to solve the MPBS problem. Extensive experiments on two real-world datasets show that our method surpasses the state-of-the-art (SOTA) by an average of 23.6% and 41.8% respectively in the comprehensive score of four optimization objectives.