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

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