<![CDATA[In Zimbabwe, the commercial forestry industry is reliant on a large proportion of outsourced harvest and milling contractors whereas existing enterprise resource planning (ERP) systems record completed transactions instead of predicting contractor failure before assigning activities. We introduce the dynamic resource allocation framework (DRAF), a risk-integrated decision-support framework that supplements the probability of contractor failure derived from XGBoost to a non-dominated sorting genetic algorithm II (NSGA-II) multi-objective optimizer. It divides contractor-block-mill combinations, minimizes cost and expected delay, and maximizes risk-adjusted timber recovery and operational reliability. We created the solution on an 828,789-record virtual ERP dataset and calibrated it to Manicaland forestry conditions and tested with statistical, heuristic and risk-free optimization baselines. Extreme gradient boosting (XGBoost) obtained a holdout receiver operating characteristic area under the curve (ROC-AUC) of 0.965, recall of 0.999, and F1 of 0.867, showing an improvement of 0.365 over logistic regression. The optimizer developed 64 complete Pareto solutions to balanced and high-recovery scenarios and uncovered a constraint-feasibility boundary for a more conservative low-risk scenario. The satisfaction score of the 30-practitioner stakeholder assessment was 4.19 out of 5.0. The results demonstrate that embedding predictive risk into an optimization objective can optimize forestry allocation decisions and suggest that some real ERP validation is required before such measures will be broadly implemented.]]>
