An Analysis of Optimization for Car PBS Scheduling Based on Greedy Strategy State Transition Algorithm

The differences in constraints between an automotive painting workshop and a final assembly workshop can lead to production scheduling disruptions, and using the Painted Body Store (PBS) in automotive manufacturing can optimize production scheduling to meet the production requirements of the final a...

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Vydáno v:Applied sciences Ročník 13; číslo 10; s. 6194
Hlavní autoři: Yu, Fengxiao, Peng, Yipu, Li, Jian, Zhou, Guangqi, Chen, Li
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.05.2023
Témata:
Algorithms
Automobiles
automotive PBS scheduling
Efficiency
Genetic algorithms
greedy strategy
Manufacturing
multi-objective mixed-integer optimization
Optimization algorithms
Scheduling
state transition algorithm
Transportation equipment industry
vehicle sequencing
Vehicles
Workshops
ISSN:2076-3417, 2076-3417
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Shrnutí:The differences in constraints between an automotive painting workshop and a final assembly workshop can lead to production scheduling disruptions, and using the Painted Body Store (PBS) in automotive manufacturing can optimize production scheduling to meet the production requirements of the final assembly workshop. This is achieved by establishing a multi-objective mixed-integer optimization scheduling model for PBS and solving the model using a state transition algorithm based on the greedy strategy; finally, a comparison was made with the classical genetic algorithm. The results show that the optimal objective function is achieved when the body sequence movement iterations for data sets N and R are 34 and 54, respectively. At this point, the highest objective function scores are 23.46 and 49.79, and vehicles entering the PBS scheduling system from the painting-to-assembly line require 31 and 38 sequence adjustments, respectively. Compared with the classical genetic algorithm, the simulation results show that the state transition algorithm based on the greedy strategy is significantly better; it can also perform well in discrete sequence movement operations, demonstrating strong global search capabilities and fast convergence. It provides a new approach for optimizing vehicle scheduling in the automotive painting-to-assembly line.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app13106194