Shahid Beheshti University Journal of Industrial Management Perspective 2251-9874 5 2 2015 08 23 Marketing Strategies Selection for Supply Chain Management under Uncertainty Propagation Marketing Strategies Selection for Supply Chain Management under Uncertainty Propagation 33 62 87256 FA Aliakbar Hasani Faculty member, Shahrood University of technology. Journal Article 2019 09 24 In this paper, we consider a closed-loop supply chain supplying products to markets and an after-sales supply chain providing spare parts to fulfill after-sales commitments. Uncertainties of product demand and facility capacity are considered via using continuous probability distribution function and are formulated in the form of a nonlinear mixed integer programming model. In addition, marketing strategies selection for supply chain management and reliable flow creation during the planning periods are considered. Here, we propose a comprehensive mathematical model for determining the best marketing strategies and preserving reliable flow dynamics throughout the chains’ networks. A new memetic algorithm is developed that incorporates genetic algorithm and adaptive variable neighborhood search to find the best solutions. Efficiency of the proposed memetic algorithm is evaluated by comparing its performance with two other solution algorithms. The proposed model and its solution approach are tested using data from an engine production company. We derive some managerial insight by analyzing the correlations among the marketing strategies and how they affect each other. In this paper, we consider a closed-loop supply chain supplying products to markets and an after-sales supply chain providing spare parts to fulfill after-sales commitments. Uncertainties of product demand and facility capacity are considered via using continuous probability distribution function and are formulated in the form of a nonlinear mixed integer programming model. In addition, marketing strategies selection for supply chain management and reliable flow creation during the planning periods are considered. Here, we propose a comprehensive mathematical model for determining the best marketing strategies and preserving reliable flow dynamics throughout the chains’ networks. A new memetic algorithm is developed that incorporates genetic algorithm and adaptive variable neighborhood search to find the best solutions. Efficiency of the proposed memetic algorithm is evaluated by comparing its performance with two other solution algorithms. The proposed model and its solution approach are tested using data from an engine production company. We derive some managerial insight by analyzing the correlations among the marketing strategies and how they affect each other. https://jimp.sbu.ac.ir/article_87256_9fb21b1eba2d011fd0f7af5bab34765b.pdf