Designing an optimal supply chain network (SCN) is an NP-hard and highly nonlinear problem; therefore, this problem may not be solved efficiently using conventional optimization methods. In this article, we propose a genetic algorithm (GA) approach with segment-based operators combined with a local search technique (SHGA) to solve the multistage-based SCN design problems. To evaluate the performance of the proposed algorithm, we applied SHGA and other competing algorithms to SCNs with different features and different parameters. The results obtained show that the proposed algorithm outperforms the other competing algorithms.
Designing an optimal supply chain network (SCN) is an NP-hard and highly nonlinear problem; therefore, this problem may not be solved efficiently using conventional optimization methods. In this article, we propose a genetic algorithm (GA) approach with segment-based operators combined with a local search technique (SHGA) to solve the multistage-based SCN design problems. To evaluate the performance of the proposed algorithm, we applied SHGA and other competing algorithms to SCNs with different features and different parameters. The results obtained show that the proposed algorithm outperforms the other competing algorithms.
| Subjects | Engineering |
|---|---|
| Journal Section | Research Article |
| Authors | |
| Publication Date | November 1, 2013 |
| IZ | https://izlik.org/JA34EM37KU |
| Published in Issue | Year 2013 Volume: 10 Issue: 2 |