Forest harvesting planning under uncertainty: a cardinality-constrained approach
提出一种基于基数约束的鲁棒优化模型，用于解决木材供应链中采伐规划的不确定性问题，通过调整鲁棒性水平来应对未来扰动。
Harvesting planning (HP) is a key tactical decision in lumber supply chains. Harvesting areas in the forests are divided into different blocks with different types and quantities of raw materials (logs). Predicting the availability of raw materials in each block along with log demand is impossible in this industry. Hence, incorporating uncertainty into the HP problem is essential in order to obtain robust plans that do not drastically fluctuate in the presence of future perturbations in the forest and log market. In this paper, we propose a robust harvesting planning model formulated based on cardinality-constrained method. The latter provides some insights into the adjustment of the level of robustness of the harvesting plan over the planning horizon and protection against uncertainty. An extensive set of experiments based on Monte-Carlo simulation is also conducted in order to better validate the proposed robust optimisation approach.