Designing a closed loop supply chain under conditions of uncertainty and partial disruptions

The challenge of designing a Closed-Loop Supply Chain (CLSC) under conditions of uncertainty and partial disruptions is complex and demanding. The concept of a CLSC involves integrating reverse logistics into the traditional forward supply chain to establish a sustainable and environmentally friendly system. However, uncertainties and partial disruptions create significant obstacles to achieving an efficient and dependable CLSC. In order to address these challenges, the concept of chance constraint is introduced, allowing for the consideration of probabilistic uncertainties in decision-making. The goal is to develop a robust CLSC model capable of effectively managing uncertain parameters such as demand, rate of return, and product quality. The Markowitz method is utilized to address uncertainty in the objective function by combining the mean with a coefficient of standard deviation. The study's results demonstrate that incorporating uncertainty into the model leads to increased profitability compared to the deterministic model. The uncertain model is more responsive to demands and considers the dynamics of confidence inventory, leading to improved decision-making. Strategic decisions, such as the number of production, distribution, and destruction facilities, remain consistent in both models. However, the capacity of destruction centers in the uncertain model is slightly smaller due to the consideration of uncertain product quality. Furthermore, incorporating uncertainty into the model has contributed to enhancing the model's clarity and facilitating improved decision-making. This increase in profitability can be attributed to the model's heightened responsiveness to demands, as well as its dynamic approach to managing confidence inventory.

Keywords:

Stochastic programming , CLSC , Uncertainty , Disruption