Abstract

Abstract
The forest-to-bioenergy supply chain is significantly vulnerable to natural disruptions, including wildfires, heavy snowfall, and windstorms. The increased occurrence of these disruptive events has caused severe challenges in forest biomass harvesting and transportation processes, which are difficult to manage. With the need to support decision-makers in designing resilient supply chains (SCs), we propose a Decision Support System (DSS) combining a two-stage stochastic programming framework with various flexibility mechanisms, such as dynamic network reconfiguration and operations postponement. The DSS incorporates an AI-based methodology to identify the most appropriate datasets and resilience metrics, capturing different supply chain dimensions (supply, demand, and operations). This integrated framework supports the selection of effective resilience-enhancing strategies to mitigate large-scale disruptions, with a particular focus on wildfires. The proposed approach is applied in a real case study in Portugal, where the most significant risk factor is wildfires. We perform computational studies and sensitivity analysis to evaluate the applicability and performance of the model and to drive managerial insights. The results show that adopting the model solutions can significantly reduce supply chain logistics and operational costs under more severe disruptive scenarios. Moreover, the results indicate up to a 60% increase in the tons of forest residues that can be removed and processed.

Abstract

Abstract

Abstract

Abstract