@article{article_306429, title={Using Segment-based Genetic Algorithm with Local Search to Find Approximate Solution for Multi-Stage Supply Chain Network Design Problem}, journal={Cankaya University Journal of Science and Engineering}, volume={10}, year={2013}, url={https://izlik.org/JA34EM37KU}, author={Rafsanjani, Marjan Kuchaki and Eskandari, Sadegh}, keywords={Supply Chain Network,Genetic Algorithm,Segment-based Operators,Local Search}, abstract={ 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. }, number={2}