Many industrial companies continue to face global uncertainties in demand and failures in supply.  The main purpose of this research is to design and optimize a supply chain network (SCN) that performs under completely uncertain environment.  In this paper, three advanced meta-heuristic algorithms based on Broyden-Fletcher-Goldfarb-Shanno (BFGS), POWELL, and Non-dominated Sorting Genetic Algorithm (NSGA-II) are used to solve the optimization problem.  A real-life case study for a steel manufacturing integrated supply chain is used to demonstrate the efficiency of the model and the solutions obtained by meta-heuristic algorithms.  The objective was to maximize the total profit of supply chain network under disruption conditions.  The presented mathematical modeling provides an understandable overview of the system for managers to make appropriate decisions to achieve the maximum profit.  Findings revealed that advanced meta-heuristic algorithms were the most efficient technique to solve the proposed model when compared with the traditional method.