A Possibilistic Fuzzy Multi-Objective Approach for Building Resilient and Sustainable Supply Chain Network under Uncertainty

Abstract

Sustainable supply chain network design is a rich area for academic research that is still in its infancy and has potential to affect supply chain performance. Increasing regulations for carbon and waste management are forcing firms to consider their supply chains from ecological and social objectives, but in reality, however, facilities and the links connecting them are disrupted from time to time, due to poor weather, natural or manmade disasters or a combination of any other factors. Supply chain systems drop their sustainability objectives while coping with these unexpected disruptions. Hence, the new challenges for supply chain managers are to design an efficient and effective supply chain network that will be resilient enough to bounce back from any disruption and that also should have sufficient vigilance to offer same sustainability under a disruption state. This study focuses on ecological sustainability, because an environmental focus in a supply chain system is more important and also links with other pillars of sustainability, as the products need to be produced, packed and transported in an ethical way, which should not harm social balance and the environment. Owing to importance of the considered issue, this dissertation attempts to introduce a network optimization model for a resilient and sustainable supply chain network by incorporating (1) sustainability via carbon emissions and embodied carbon footprints and (2) resilience by incorporating location-specific risks. To attain this aim three objectives are considered. Initially, a multi objective resilient and sustainable supply chain network model is developed considering economic, sustainable, and resilient objectives. Secondly, a possibilistic fuzzy optimization solution methodology is proposed. Furthermore, a fuzzy linguistic weight based interactive solution methodology is designed to consider relative importance of model objectives.