Resilience Evaluation of Entropy Regularized Logistic Networks with Probabilistic Cost

TL;DR

This paper proposes a novel method for designing resilient logistics networks using entropy regularization and probabilistic cost criteria, providing analytical resilience measures and demonstrating effectiveness through a simple network case study.

Contribution

It introduces an entropy-regularized optimization framework combined with probabilistic resilience criteria for logistics network design, enhancing robustness analysis.

Findings

Entropy regularization diversifies logistics solutions.

Resilience criteria based on probabilistic cost and divergence are effective.

Designed logistics plans show improved resilience in case study.

Abstract

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.