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An improved genetic algorithm for multi-objective painted body storage scheduling problem
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AI
DOI：10.1007/s10696-025-09654-1.png)
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Abstract
En 中文
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.
Keywords:
Car production
Painted body storage
Genetic algorithm
Scheduling optimization
Journal
F
IF:
3.2
Papers:
32
Citations:
1.1K
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