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.

中文翻译：

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应对汽车供应链中断事件造成的短缺

不可预测的破坏性事件显着增加了汽车供应链管理的难度。在本文中，我们提出了在有限规划范围内具有组件芯片替换的汽车生产规划问题。一个芯片的短缺可以通过另一种具有更高端特性的同类型芯片来弥补，但需要额外的成本。因此，汽车制造商可以将现有的芯片库存转移到在供应链中断导致短缺的情况下更有利可图的产品线。为了解决这个问题，我们提出了一个最大-最小稳健优化模型，该模型可以捕获芯片的不确定供应。我们表明，稳健模型具有混合整数规划等价性，可以直接由商业 IP 求解器求解。我们通过广泛的数值实验将最大-最小稳健模型与针对同一问题的相应确定性和两阶段随机模型进行比较。计算结果表明，最大-最小稳健模型在平均和最坏情况下的利润方面优于其他两个模型。