A supply chain network consists of facilities located in dispersed geographical locations. This network structure can be optimized to minimize total cost or total inventory by deciding the order quantities and distribution of links connecting the facilities. However, bullwhip effect (i.e., amplification of order fluctuations) is an important performance metric for supply chains because as the order variance increases in the downstream of the supply chain (e.g., distributors), the demand variance in the upstream (e.g., manufacturer) amplifies and causes inefficiencies in the supply chain. In this study, we optimize supply chain network structure for multi-product multi-period two-echelo echelon supply chain networks to minimize bullwhip. Due to nonlinear structure of the of the objective function, i.e., bullwhip effect, this paper proposes a differ differential evolution (DE) algorithms employing variable neighborhood search (VNS) (VNS) and constraint handling methods to optimize supply chain network struct structure. The proposed algorithm is tested over randomly generated test instan instances and its effectiveness is demonstrated. echelon supply chain networks to minimize bullwhip. Due to nonlinear structure of the objective function, i.e., bullwhip effect, this paper proposes a differential evolution (DE) algorithms employing variable neighborhood search (VNS) and constraint handling methods to optimize supply chain network structure. The proposed algorithm is tested over randomly generated test instances and its effectiveness is demonstrated.
