Ozgur KabadurmusMehmet Serdar ErdoğanM. Fatih Tasgetiren2025-10-062017978150904601010.1109/CEC.2017.7969390https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028507701&doi=10.1109%2FCEC.2017.7969390&partnerID=40&md5=d8e7ef574146f1418b875b8b1896be1dhttps://gcris.yasar.edu.tr/handle/123456789/9665A 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-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. © 2017 Elsevier B.V. All rights reserved.EnglishBullwhip Effect, Constraint Handling, Ensemble Of Differential Evolution, Supply Chain, Variable Neighborhood Search, Optimization, Product Design, Supply Chains, Bullwhip Effects, Constraint Handling, Differential Evolution, Differential Evolution Algorithms, Geographical Locations, Supply Chain Network, Two-echelon Supply Chain, Variable Neighborhood Search, Evolutionary AlgorithmsOptimization, Product design, Supply chains, Bullwhip effects, Constraint handling, Differential Evolution, Differential evolution algorithms, Geographical locations, Supply chain network, Two-echelon supply chain, Variable neighborhood search, Evolutionary algorithmsConference Object