Resilience Evaluation of Entropy Regularized Logistic Networks with Probabilistic Cost
This work addresses the problem of designing resilient logistics networks for disaster-prone scenarios, but it appears incremental as it builds on existing entropy regularization techniques.
The study tackled the design of resilient logistics networks by proposing a method based on entropy regularization and analytical resilience criteria using probabilistic cost and Kullback-Leibler divergence, demonstrating resilience for three logistics plans in a simple network.
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.