Coping with shortages caused by disruptive events in automobile supply chains
针对汽车供应链中芯片短缺问题，提出一种允许用高端芯片替代低端芯片的生产规划模型，采用最大最小鲁棒优化方法应对供应不确定性，实验表明该模型在平均和最差情况利润上优于确定性模型和两阶段随机模型。
Abstract Unpredictable disruptive events significantly increase the difficulty of the management of automobile supply chains. In this paper, we propose an automobile production planning problem with component chips substitution in a finite planning horizon. The shortage of one chip can be compensated by another chip of the same type with a higher‐end feature at an additional cost. Therefore, the automobile manufacturer can divert the on‐hand inventory of chips to product lines that are more profitable in the event of shortages caused by supply chain disruptions. To cope with this, we propose a max‐min robust optimization model that captures the uncertain supplies of chips. We show that the robust model has a mixed‐integer programming equivalence that can be solved by a commercial IP solver directly. We compare the max‐min robust model with the corresponding deterministic and two‐stage stochastic models for the same problem through extensive numerical experiments. The computational results show that the max‐min robust model outperforms the other two models in terms of the average and worst‐case profits.