Abstract
The green agri-food supply chain network (GASCN) design is critical to reduce the total transportation cost for efficient and effective supply chain management. This paper proposes a new solution based on particle swarm optimization (PSO) to find optimal solution for GASCN problem. PSO adopts transforming operator to modify particles in the population. The novelty of the transforming operator is that it can avoid applying the penalty function so that the diversity of populations is decreased. To show the efficacy of the algorithm, PSO is also tested on three cases. Results show that the proposed algorithm is promising and outperforms GA by both optimization speed and solution quality, especially when the scale of problem is large.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Altiparmak, F., & Gen, M. (2006). A genetic algorithm approach for multi-objective optimization of supply chain networks. Computers and Industrial Engineering, 51(1), 197–216.
Erenguc, S. S., Simpson, N. C., & Vakharia, A. J. (1999). Integrated production/distribution planning in supply chains: an invited review. European Journal of Operational Research, 115(2), 219–236.
Pontrandolfo, P., & Okogbaa, O. G. (1999). Global manufacturing: A review and a framework for planning in a global corporation. International Journal of Production Economics, 37(1), 1–19.
Amiri, A. (2006). Designing a distribution network in a supply chain system: Formulation and efficient solution procedure. European Journal of Operational Research, 171(2), 567–576.
Costa, A., Celano, G., Fichera, S., & Trovato, E. (2010). A new efficient encoding/decoding procedure for the design of a supply chain network with genetic algorithms. Computers and Industrial Engineering, 59(4), 986–999.
Prakash, A., Chan, F. T. S., Liao, H., & Deshmukh, S. G. (2012). Network optimization in supply chain: A KBGA approach. Decision Support Systems, 52(2), 528–538.
Altiparmak, F., Gen, M., Lin, L., et al. (2006). A genetic algorithm approach for multi-objective optimization of supply chain networks. Computers and Industrial Engineering, 51(1), 196–215.
Henry, C. W. L. (2009). Cost optimization of the supply chain network using Genetic Algorithms. IEEE Transactions on Knowledge and Data Engineering, 99(1), 1–36.
Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of IEEE international conference on neural networks (pp. 1942–1948). Piscataway, NJ: IEEE Press.
Heo, J., Lee, K., & Garduno-Ramirez, R. (2006). Multiobjective control of power plants using particle swarm optimization techniques. IEEE Transactions on Energy Conversion, 21(2), 552–561.
Acknowledgments
This work is supported by the Special Funds for Doctoral Research Project of Guangdong University of Education under Grant No. 2012ARF05, the Natural Science Foundation of Guangdong Province of China under Grant No. S2012020011067 and the National High Technology Research and Development Program of China (863) under Grant No. 2012AA101701.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Tao, Q., Huang, Z., Gu, C., Zhang, C. (2014). Optimization of Green Agri-Food Supply Chain Network Using Particle Swarm Optimization Algorithm. In: Wong, W.E., Zhu, T. (eds) Computer Engineering and Networking. Lecture Notes in Electrical Engineering, vol 277. Springer, Cham. https://doi.org/10.1007/978-3-319-01766-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-01766-2_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01765-5
Online ISBN: 978-3-319-01766-2
eBook Packages: EngineeringEngineering (R0)