This chapter is to present a successful industry case applying Genetic Algorithms (GAs). The case has applied GAs for the purpose of optimizing the total cost of a multiple sourcing supply chain system. The system is characterized by a multiple sourcing model with stochastic demand. A mathematical model is adopted to describe the stochastic inventory with the many-to-many demandsupplier network problem and it simultaneously constitutes the inventory control and transportation parameters as well as price uncertainty factors. Genetic Algorithms are applied to derive optimal solutions through the optimization process. A numerical example and its solution demonstrate the detail solution procedure based on Genetic Algorithms and shows that GAs are able to solve such a di.cult problem e.ciently and easily. Further research is also discussed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Altiparmak, F., Gen, M., Lin, L. and Paksoy, T., (2006), A genetic algorithm approach for multi-objective optimization of supply chain networks, Computer & Industrial Engineering, Vol. 51, pp. 197–216.
Chan, C. H., Cheng, C. B. and Huang, S. W., (2006), Formulating ordering policies in a supply chain by genetic algorithm, International Journal of Modelling and Simulation, Vol. 26, No. 2, pp. 129–135.
Droste, S., Jansen, T. and Wegener, I., (2002), Optimization with randomized search heuristics – The (A)NFL theorem, realistic scenarios, and difficult function, Theoretical Computer Science, Vol. 287, No. 1, pp. 131–144.
Ganeshan, R., Tyworth, J. E. and Guo, Y., (1999), Dual sourced supply chains: The discount supplier option, Transportation Research Part, E 35, pp. 11–23.
Ghodsypour, S. H. and O’Brien, C., (2001), The total cost of logistics in supplier selection, under conditions of multiple sourcing, multiple criteria and capacity constraint, International Journal of Production Economics, Vol. 73, pp. 15–27.
Han, C. and Damrongwongsiri, M., (2005), Stochastic modelling of a two-echelon multiple sourcing supply chain system with genetic algorithm, Journal of manufacturing Technology Management, Vol. 16, No. 1, pp. 87–108.
Holland, J. H., (1975), Adaptation in natural and artificial systems, Ann Arbor: University of Michigan Press.
Huang, M., Ding, J., Ip, W. H., Yung, K. L., Liu, Z. and Wang, X., (2006), The research on the optimal control strategy of a serial supply chain based on GA, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 4221 LNCS – I, Advances in Natural Computation – Second International Conference, ICNC 2006, Proceedings, pp. 657–665.
Jeong, B., Jung, H. and Park, N., (2002), A computerized causal forecasting system using genetic algorithms in supply chain management, Journal of Systems and Software, Vol. 60, No. 3, pp. 223–237.
Kim, B., Leung, J. M. Y., Park, K. T., Zhang, G. and Lee, S, (2002), Configuring a manufacturing firm’s supply network with multiple suppliers, IIE Transactions, Vol. 34, pp. 663–677.
Konak, A., Coit, D. W. and Smith, A. E., (2006), Multi-objective optimization using genetic algorithms: A tutorial, Reliability Engineering & System Safety, No. 91, pp. 992–1007.
Maiti, M. K. and Maiti, M., (2007), Two-stage inventory model with lot-size dependent fuzzy lead-time under possibility constraints via genetic algorithm, European Journal of Operational Research, Vol. 179, pp. 352–371.
Minner, S., Diks, E. B. and de Kok, A. G., (1999), Two-echelon inventory system with supply lead time flexibility, Faculty of Economics and Management, Ottovon-Guericke-University Magdeburg. IIE-Transactions.
Naso, D., Surico, M., Turchiano, B. and Kaymak, U., (2007), Genetic algorithms for supply-chain scheduling: A case study in the distribution of ready-mixed concrete, European Journal of Operational Research, Vol. 177, No. 3, pp. 2069–2099.
O’Donnell, T., Maguire, L. and Humphreys, P., (2006), Minimizing the bullwhip effect in a supply chain using genetic algorithms, International Journal of Production Research, Vol. 44, No. 8, pp. 1523–1543.
Ravindran, A., Ragsdell, K.M. and Reklaitis, G.V., (2006), Engineering optimization: methods and applications, Wiley, Hoboken, NJ.
Wang, K., (2005), Applied Computational Intelligence in Intelligent Manufacturing Systems, Advanced Knowledge International Pty Ltd, Australia.
Xing, L., Chen, Y. and Cai, H., (2006), An intelligent genetic algorithm designed for global optimization of multi-minima functions, Applied Mathematics and Computation, Vol. 178, pp. 355–371.
Yokoyama, M., (2002), Integrated optimization of inventory-distribution systems by random local search and a genetic algorithm, Computer & Industrial Engineering, Vol. 42, No. 2–3, pp. 175–188.
Zhang, W., Lu, Z. and Zhu, G., (2006), Optimization of process route by Genetic Algorithms, Robotics and Computer-Integrated Manufacturing, Vol. 22, pp. 180–188.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Wang, K., Wang, Y. (2008). Applying Genetic Algorithms to Optimize the Cost of Multiple Sourcing Supply Chain Systems – An Industry Case Study. In: Yang, A., Shan, Y., Bui, L.T. (eds) Success in Evolutionary Computation. Studies in Computational Intelligence, vol 92. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76286-7_16
Download citation
DOI: https://doi.org/10.1007/978-3-540-76286-7_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76285-0
Online ISBN: 978-3-540-76286-7
eBook Packages: EngineeringEngineering (R0)