Modeling of Closed-Loop Supply Chains by Utilizing Scenario-Based Approaches in Facing Uncertainty in Quality and Quantity of Returns - Mansour Momeni (University of Tehran, 2021)
Objective: The main purpose of this research is to develop a scenario-based model to deal with the design and planning decisions of supply chain networks considering uncertainty in both quantity and quality of the returned products at their end of life era. Methods: In this approach by the help of scenarios and operation research, a mixed-integer linear programming model is applied and profit maximization is chosen to be the target of this model which integrates multi - products and multi - periods of times with different time horizons. In this model, several flows of products between entities like factories, storages, distribution/recycling/disposal centers and costumers have been taken into account that is according to the variety of entities in the network. The uncertainty associated with the quantity and quality of the used products in the reverse network which is directly affected by customers’ usage and sorting results in recycling centers, respectively, have been taken into account as the main cause of the uncertainty. Results: Of the main findings of this research which was the applicability of these kinds of approaches in real case problems, have been approved in an acceptable time and has shown that this approach can be used in cases with mathematical functions predicting their uncertainty behavior. Conclusion: Finally, the model is deployed in Steel industry of Iran with real data from factories and market to examine the model in utilizing potential locations for different entities by considering the costs, especially the lost costumers. As predicted, increase in quality of return will reduce the need for raw material and as a result, will increase the profit of the entire chain and increase in the quantity of returned might need to build more entities that would reduce the profit of the entire chain and even unprofitable at all.