Cost Optimal Production-Scheduling Model Based on VNS-NSGA-II Hybrid Algorithm—Study on Tissue Paper Mill

• Published in: Processes Vol. 10; no. 10; p. 2072
• Main Authors: Zhang, Huanhuan, Li, Jigeng, Hong, Mengna, Man, Yi, He, Zhenglei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Algorithms; Analysis; Economic aspects; Efficiency; Energy consumption; Genetic algorithms; Manufacturing costs; Multiple objective analysis; Optimization algorithms; Optimization models; Paper industry; Paper mills; Papermaking; Production costs; Production management; Production scheduling; Pulp & paper mills; Scheduling; Search algorithms; Software
• ISSN: 2227-9717; 2227-9717
• DOI: 10.3390/pr10102072

With the development of the customization concept, small-batch and multi-variety production will become one of the major production modes, especially for fast-moving consumer goods. However, this production mode has two issues: high production cost and the long manufacturing period. To address these issues, this study proposes a multi-objective optimization model for the flexible flow-shop to optimize the production scheduling, which would maximize the production efficiency by minimizing the production cost and makespan. The model is designed based on hybrid algorithms, which combine a fast non-dominated genetic algorithm (NSGA-II) and a variable neighborhood search algorithm (VNS). In this model, NSGA-II is the major algorithm to calculate the optimal solutions. VNS is to improve the quality of the solution obtained by NSGA-II. The model is verified by an example of a real-world typical FFS, a tissue papermaking mill. The results show that the scheduling model can reduce production costs by 4.2% and makespan by 6.8% compared with manual scheduling. The hybrid VNS-NSGA-II model also shows better performance than NSGA-II, both in production cost and makespan. Hybrid algorithms are a good solution for multi-objective optimization issues in flexible flow-shop production scheduling.