A Possibilistic Reliable and Responsive Closed Loop Supply Chain Network Design Model under Uncertainty

Reliability of supply chain networks is an important issue affecting customer satisfaction and profitability of organizations. However, occurrence of disruptions such as flood, earthquake and fire could ruin performance of supply chains. Uncertainty of parameters is another important factor that could lower quality of long-term plans of companies. Hence, uncertainty of parameters and disruption strike are important issues adversely influencing reliability of networks. Also, responsiveness of supply chains is a significant matter that should be considered carefully while designing distribution networks. Responsiveness could increase customer loyalty and satisfaction that could result in increasing market share of companies and their long-term planned benefit. Regarding alluded matters, the aim of this paper is designing a reliable forward reverse supply chain network that minimizes total costs of network design along with maximization of total responsiveness of distribution network. Extended closed-loop network is capable of considering environmental issues by caring about end-of-life products. Designing reverse supply chain network aside with forward ones could decrease bad environmental impact of end-of-life products. Notably, to cope with adverse effects of disruptions, a scenario-based approach is suggested that enables considering partial and complete disruption of capacity of facilities. Additionally, an effective possibilistic programming method is applied to appropriately control uncertainty of parameters. As quality of raw materials is important to produce high-quality products, minimum acceptable quality level of raw materials is considered in extended model to maximize customer satisfaction. Finally, it should be noted that designed test problems show appropriate performance of suggested model and its applicability in real world case studies. Extended model is solved regarding different risk-aversion levels and sensitivity analysis is performed for different parameters of network design that shows effectual performance of proposed model.