THE ROBUST OPTIMIZATION OF MULTI-OBJECTIVE GREEN CLOSED LOOP SUPPLY CHAIN UNDER UNCERTAINTY (CASE STUDY: INDUSTRIAL BREAD

Lack of resources, ecosystems at risk, and climate change have a great impact on the living environment. One of the most important issues and challenges that countries, including our country, face is the waste of resources. In the past decade, due to the increasing importance of economic competitiveness, legal pressures, environmental concerns in the context of old products, and social impacts, the issue of a closed-loop supply chain has attracted many researchers. Increasing the eciency and eectiveness of supply chain activities is one of the sustainable competitive advantages for companies. Also, companies and organizations are not only aware of environmental factors but are also aware of the revenues of collecting and recycling their used products. The closedloop supply chain is a way of considering the recycling of products to control environmental barriers. This research aims to develop a robust optimization approach for designing a supply chain network in industrial bread. A multi-objective integrated integer linear programming model is presented as a single-product, single-cycle, and multi-capacity. In this model, two economic and environmental objectives are examined under certainty and uncertainty conditions. A balance between the goals of the equilibrium is established and the model is tested in dierent sizes and uncertainties by using the Torabi and Hassini (TH) method. The results demonstrate that in all cases, the robust approach overcomes the deterministic approach based on the mean value of the objective function. Regarding the standard deviation, the robust approach in the problem of 6*10*10*6 with uncertainties =0.2 and =1, the problem of 9*15*15*9 with uncertainties =0.2 and =0.5, and the problem of 12*20*20*12 with uncertainties =0.2, =0.5 and =1, extremely overcome but in other cases, the deterministic approach has better outcomes. A robust strategy model achieves higher quality and better performance than the deterministic strategy in large problems and higher uncertainty. Due to the size of the problem and the uncertainty, in most cases, the gap between these two approaches increases with respect to both coecients of performance.