Abstract
The practical relevance and challenging nature of the Vehicle Routing Problem (VRP) have motivated the Operations Research community to consider different practical requirements and problem variants throughout the years. However, businesses still face increasingly specific and complex transportation re-quirements that need to be tackled, one of them being synchronisation. No literature contextualises syn-chronisation among other types of problem aspects of the VRP, increasing ambiguity in the nomenclature used by the community. The contributions of this paper originate from a literature review and are three-fold. First, new conceptual and classification schemas are proposed to analyse literature and re-organise different interdependencies that arise in routing decisions. Secondly, a modelling framework is presented based on the proposed schemas. Finally, an extensive literature review identifies future research gaps and opportunities in the field of VRPs with synchronisation.(c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

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
Forest management and operations planning involve complex decisions that integrate ecological knowledge, spatial data, and analytical tools to balance sustainable resource use with risk mitigation. Disturbances such as storms, diseases, and wildfires increasingly disrupt forest ecosystems and value chains. The timely removal, processing, and delivery of forest residues to bioenergy facilities are essential to reduce wildfire risk, prevent disease spread, and ensure operational continuity for forest managers and owners. This study presents a decision-support approach to address supply uncertainty caused by wildfires within the forest-to-bioenergy value chain. The methodology first generates multiple raw material variability scenarios using a fire simulation model, then clusters them according to post-fire biomass availability and probability of occurrence. These clusters are integrated into a two-stage stochastic optimization model incorporating a Conditional Value-at-Risk (CVaR) metric. Results show that the stochastic model with CVaR achieves the lowest total cost while ensuring complete processing of biomass under the most severe wildfire scenarios. The findings highlight the value of flexible and risk-aware planning strategies for forest operations, supporting decision-makers in balancing investments in processing capacity, cost efficiency, and post-disturbance resource utilization.