Optimizing Painting Sequence Scheduling Based on Adaptive Partheno-Genetic Algorithm

In this paper, we formulate and solve a novel real-life large-scale automotive parts paint shop scheduling problem, which contains color arrangement restrictions, part arrangement restrictions, bracket restrictions, and multi-objectives. Based on these restrictions, we construct exact constraints an...

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Veröffentlicht in:Processes Jg. 9; H. 10; S. 1714
Hauptverfasser: Yang, Jun, Sun, Tong, Huang, Xiuxiang, Peng, Ke, Chen, Zhongxiang, Qian, Guoguang, Qian, Zekai
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.10.2021
Schlagworte:
Adaptive algorithms
Algorithms
Automation
Automotive parts
Brackets
Color
Constrictions
Design
Genetic algorithms
Integer programming
Linear programming
Manufacturing
Mixed integer
Optimization
Restrictions
Robots
Schedules
Scheduling
Variables
ISSN:2227-9717, 2227-9717
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Zusammenfassung:In this paper, we formulate and solve a novel real-life large-scale automotive parts paint shop scheduling problem, which contains color arrangement restrictions, part arrangement restrictions, bracket restrictions, and multi-objectives. Based on these restrictions, we construct exact constraints and two objective functions to form a large-scale multi-objective mixed-integer linear programming problem. To reduce this scheduling problem’s complexity, we converted the multi-objective model into a multi-level objective programming problem by combining the rule-based scheduling algorithm and the adaptive Partheno-Genetic algorithm. The rule-based scheduling algorithm is adopted to optimize color changes horizontally and bracket replacements vertically. The adaptive Partheno-Genetic algorithm is designed to optimize production based on the rule-based scheduling algorithm. Finally, we apply the model to the actual optimization problem that contained 829,684 variables and 137,319 constraints, and solved this problem by Python. The proposed method solves the optimal solution, consuming 575 s.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:2227-9717
2227-9717
DOI:10.3390/pr9101714