Sustainable fertilizer supply chain network design using evolutionary-based resilient robust stochastic programming. (December 2022) - Rabbani, Motahareh; Mohammad Hadji Molana, Seyyed; Mojtaba Sajadi, Seyed; Hossein Davoodi, Mohammad
Highlights: Investigating the challenges of P-SCND mathematically from the whole supply chain perspective. Proposing a multi-objective model for P-fertilizers to design a sustainable-resilient P-SCND. Improving the sustainability of P-SCND with economic, social, and environmental considerations. Presenting a resilience robust stochastic programming (RRSP) to handle uncertainties and disruptions. Utilizing an evolutionary algorithm based on genetic algorithm (GA) to solve the P-SCND. Abstract: Phosphorus is an unsustainable substance that plays an essential role in modern agricultural systems and crop yield. Due to phosphorus growing demand and the importance of sustainable application of this critical resource, there is increasing concern about its supply chain network sustainability and resiliency. In this paper, a multi-objective, multi-product, multi-period mathematical model is developed for the sustainable phosphorus supply chain management in an uncertain environment. The parametric uncertainties such as demand and supply are aggravated by disruptions with devastating effects on strategic, tactical, and operational decisions. Given the potential adverse effects of the phosphorus supply chain on the environment and human beings, a sustainable-resilient supply chain network of the fertilizer industry is designed by considering the related environmental, social, and economic challenges of the phosphorus managing. A reactive strategy is adapted to encounter the disruptions and breakdowns along with the network, while a robust stochastic programming is extended and solved using genetic algorithm to cope with the real-world uncertainties. The proposed model effectively controls the uncertainty and risk-aversion of output decisions and confronts the adverse effects of disruptions. The effectiveness and applicability of the model are validated through a real case study. Besides, the performance and reliability of the model are proved by the realization under new scenarios. The results indicate that the proposed model performs well in capturing real-world uncertainties and promoting the sustainability and resiliency of the network.