Building robust logistics networks against facility disruptions has been gaining more attention from both researchers and practitioners. However, in pursuit of high built-in network robustness, one usually has to increase the network redundancy and complexity, which adds to the difficulty of management and induces additional operating costs. In this paper, we aim to balance the trade-off between robustness and complexity in the logistics network design problem and propose a model that explicitly considers the demand uncertainty. Due to the non-convexity of our model, we present a linear reformulation method that transforms the model into a MILP. From our numerical studies, in some cases, we can simultaneously enhance robustness and reduce complexity. When the demand uncertainty increases, the network will become less robust. It will require more backup links to guarantee a given robustness requirement. Consequently, the network can exhibit higher complexity and incur additional costs. We also observe it might be possible to dramatically reduce the complexity at a reasonable cost. One can achieve a relatively simple and robust network by only maintaining backup links for customer zones with large demand volumes. Finally, we provide discussions on managerial implications.

建立强大的物流网络以应对设施中断的问题已引起研究人员和从业人员的更多关注。然而，为了追求高内置的网络鲁棒性，通常必须增加网络冗余度和复杂性，这增加了管理难度并引起额外的运营成本。在本文中，我们旨在平衡物流网络设计问题中鲁棒性和复杂性之间的权衡，并提出一个明确考虑需求不确定性的模型。由于我们模型的非凸性，我们提出了一种线性重构方法，可以将模型转换为MILP。通过我们的数值研究，在某些情况下，我们可以同时增强鲁棒性并降低复杂性。当需求不确定性增加时，网络将变得不那么健壮。它将需要更多的备份链接来确保给定的健壮性要求。因此，网络可能表现出更高的复杂性并招致额外的费用。我们还观察到有可能以合理的成本大大降低复杂性。通过仅为需求量大的客户区域维护备份链接，就可以实现相对简单而强大的网络。最后，我们提供有关管理意义的讨论。