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In recent years, the optimal design of the workshop schedule has received much attention with the increased competition in the business environment. As a strategic issue, designing a workshop schedule affects other decisions in the production chain. The purpose of this thesis is to design a three-objective mathematical model, with the objectives of minimizing work completion time, work delay time and energy consumption, considering the importance of businesses attention to reduce energy consumption in recent years. The developed model has been solved using exact solution methods of Weighted Sum (WS) and Epsilon Constraint (Ɛ) in small dimensions using GAMS software. These problems were also solved in large-scale problems with NSGA-II and SFLA meta-heuristic algorithms using MATLAB software in single-objective and multi-objective mode due to the NP-Hard nature of this group of large and real dimensional problems. The standard BRdata set of problems were used to investigate the algorithms performance in solving these problems so that it is possible to compare the algorithms performance of this research with the results of the algorithms used by other researchers. The obtained results show the relatively appropriate performance of these algorithms in solving these problems and also the much better and more optimal performance of the NSGA-II algorithm compared to the performance of the SFLA algorithm.

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This paper models a closed-loop supply chain network problem for hazardous products in the face of demand uncertainty and variable costs. The designed model includes a set of suppliers, production centers, distribution, recycling, disposal, collection and end customers in which strategic and tactical decisions are made simultaneously. Among the decisions made in this paper is the location of production, distribution and collection centers and determining the optimal amount of product flow between the levels of the supply chain network. Methodology: In this paper, the Epsilon constraint method is used to solve a multi-objective model in GMAS software. This article also uses uniform data to solve the problem. Findings: The results of solving the model with fuzzy robust optimization method show that with increasing the uncertainty rate and also reducing the transfer time of hazardous products, the total network costs as well as the amount of greenhouse gas emissions have increased. Also, the study of Pareto front to optimize the total design costs and the amount of greenhouse gas emissions shows that by reducing the amount of greenhouse gas emissions in the network, the costs related to location and routing increase. Originality/Value: In this paper a fuzzy robust optimization is used in closed-loop supply chain network model for hazardous products (Lead-Acid Battery).