Applying genetic algorithms to optimize the cost of multiple sourcing supply chain systems - An industry case study

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 efficiently and easily. Further research is also discussed. © 2008 Springer-Verlag Berlin Heidelberg.