Resilience Evaluation of Entropy Regularized Logistic Networks with Probabilistic Cost

Abstract

The demand for resilient logistics networks has increased because of recent disasters. With regard to optimization problems, entropy regularization is a powerful tool for the diversification of a solution. In this study, we devised a method for designing a resilient logistics network based on entropy regularization. Moreover, we developed 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, the proposed method was implemented using a simple logistics network, and the resilience of three logistics plans designed using entropy regularization was demonstrated. Consequently, it is confirmed that our criteria can be used to evaluate resilience to logistics disruptions.