Using Segment-based Genetic Algorithm with Local Search to Find Approximate Solution for Multi-Stage Supply Chain Network Design Problem

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.

Keywords

• Supply Chain Network,Genetic Algorithm,Segment-based Operators,Local Search

References

1. F. Altiparmak, M. Gen, L. Lin, I. Karaoglan, A steady-state genetic algorithm for multi-product supply chain network design, Computers & Industrial Engineering, 56, (2009), 521–537.
2. A. Costa, G. Celano, S. Fichera, E.Trovato, A new efficient encoding/decoding procedure for the design of a supply chain network with genetic algorithms, Computers & Industrial Engineering, 59(4), (2010), 986–999.
3. M. Gen, R. Cheng, Genetic algorithms and engineering optimization. New York: John Wiley and Sons, (2000).
4. M .Gen, F. Altiparmak,L. Lin, A genetic algorithm for two-stage transportation problem using priority-based encoding. OR Spectrum, 28, (2006), 337–354.
5. M. Hajiaghaei-Keshteli, The allocation of customers to potential distribution centers in supply chain networks: GA and AIA approaches, Applied Soft Computing, 11(2), (2010), 2069–2078.
6. J.H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, (1975).
7. M. Kaya, The effects of a new selection operator on the performance of a genetic algorithm, Applied Mathematics and Computation, 217(19), (2011), 7669–6778.
8. M. Kaya, The effects of two new crossover operators on genetic algorithm performance, Applied Soft Computing 11(1), (2011), 881–890.

9. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Program. Spring-Verlag, (1994).
10. B. L. Miller, D. E. Goldberg, Genetic Algorithms, Tournament Selection, and the Effects of Noise, Complex Systems, 9, (1995), 193–212.
11. M. S. Pishvaee, M .Rabbani, A graph theoretic-based heuristic algorithm for responsive supply chain network design with direct and indirect shipment. Advances in Engineering Software, 42(3), (2010), 57–63.
12. S. N. Sivanandam, S. N. Deepa, Introduction to Genetic Algorithms, New York: Springer Berlin Heidelberg, (2008).
13. A. Syarif, Y. Yun, M. Gen. Study on multi-stage logistics chain network: A spanning tree-based genetic algorithm approach. Computers & Industrial Engineering, 43(1-2), (2002), 299–314.
14. L. C. Wang, T. L. Chen, Y.Y. Chen, H. Y. Miao, S. C. Lin, S. T. Chen, Genetic algorithm approach for multi–objective optimization of closed–loop supply chain network, Proceedings of the Institute of Industrial Engineers Asian Conference 2013, (2013), 149–156.
15. M. J. Yao, H. W. Hsu, A new spanning tree–based genetic algorithm for the design of multi–stage supply chain networks with nonlinear transportation costs, Optimization and Engineering, 10(2), (2009), 219–237.
16. W. C. Yeh, A hybrid heuristic algorithm for the multistage supply chain network problem, Int J AdvManufTechnol, 26(5–6), (2005), 675–685.
17. Y. Yun, C. Moon, D. Kim, Hybrid genetic algorithm with adaptive local search scheme for solving multistage-based supply chain problems, Computers & Industrial Engineering, 56(3), (2009), 821–838.