Global Biomass Supply Chain Resilience Optimization Based on Sustainability Pillars

The global biomass supply chain (GBSC) lacks resilience and faces a global disruption during and after the COVID-19 pandemic. Maritime transportation is becoming costlier economically and environmentally. This work focuses on the sustainability pillars of the GBSC maritime transportation network based on sustainable development goals. To do this, it aims at developing a multi-objective mathematical model to optimize the GBSC resilience by implementing Nondominated Sorting Genetic Algorithm II (NSGA-II). Two leading crude palm oil suppliers worldwide, Indonesia and Malaysia, were selected to test the developed model and suggested approach. Results showed that the optimum economic growth of GBSC resiliency is achieved by higher environmental emissions (63%). The economic and social cost is also increased by 61% to achieve maximum economic profit. In case of Indonesia, it was found that the best combination of maximum mass flow and low environmental emissions can be achieved when shipping the biomass to India, Spain, and the Netherlands. The developed model and suggested method in this work would help to improve the resiliency of other supply chain disruptions worldwide after the COVID-19 pandemic.