Recently, large companies have shown a growing tendency to enhance the reliability and sustainability of their supply chains to increase customers' satisfaction in terms of on-time fulfillment of demands and to be compatible with environmental regulations. Therefore, finding the best approaches to achieve companies' goals is a crucial concern in supply chain management, and the majority of organizations prefer to cooperate with reliable and sustainable companies. In designing a supply chain, the supplier selection problem, order size identification, and order allocation are midterm decisions that are needed to be made separately. To this end, three levels of the supply chain, i.e., suppliers, central warehouses, and wholesalers are considered. In the first level, to address the sustainable supplier selection problem, a novel hybrid approach based on the fuzzy logic is implemented. This approach applies the Fuzzy Analytic Network Process (FANP) method to ranking criteria and sub-criteria, the fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) is applied to identification of the relationships among the main criteria, and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to prioritizing the suppliers. After prioritizing the suppliers, the obtained weights are considered as the input of a tri-objective model designed to optimize the proposed supply chain. The objectives are minimization of the total cost of the chain, maximization of the weighted value of products by taking the account of suppliers' priorities, and maximization of the reliability of the supply chain. Weighted Goal Programming (WGP) method is then used to deal with multi-objectiveness. To assess the applicability of the suggested methodology, a case study of the lamp supply chain was considered and solved using GAMS/CPLEX solver and optimal policies based on suppliers' sustainability. The reliability of the supply chain was tested, and sensitivity analyses were also performed on the main parameters of the model. (C) 2019 Elsevier Ltd. All rights reserved.