  The demand for resilient logistics networks has increased because of recent
disasters. When we consider optimization problems, entropy regularization is a
powerful tool for the diversification of a solution. In this study, we proposed
a method for designing a resilient logistics network based on entropy
regularization. Moreover, we proposed a method for analytical resilience
criteria to reduce the ambiguity of resilience. First, we modeled the logistics
network, including factories, distribution bases, and sales outlets in an
efficient framework using entropy regularization. Next, we formulated a
resilience criterion based on probabilistic cost and Kullback--Leibler
divergence. Finally, our method was performed using a simple logistics network,
and the resilience of the three logistics plans designed by entropy
regularization was demonstrated.

Systems and Control;Machine Learning;Systems and Control