A neutrosophic optimization model for supply chain virtualization in the circular economy using the non-dominated sorting genetic algorithm II

The internet and technology have developed so fast that the entire world is experiencing the fourth industrial revolution. However, organizations’ traditional digital capabilities are not enough to respond to the growing market needs. With the use of Internet technologies, virtualization can be used dynamically in the operational management of supply chains. As a result, supply chains can be controlled, planned, and optimized remotely and through the internet based on virtual objects instead of direct observation. This research seeks to optimize the supply chain by considering the dimensions of supply chain virtualization in the form of two objective functions, profit and processing rate. The presented model has been optimized under conditions of demand uncertainty and in a triangular Neutrosophic environment using the Non-Dominated Sorting Genetic Algorithm II. Finally, by solving a numerical example, the sensitivity analysis of the algorithm performance and its application in the model have been investigated. The results of this research showed that the profit of the virtual supply chain increases compared to the traditional supply chain due to tracking defective parts and identifying returnable products.