A study on the application of quantum-inspired black-box optimization for optimizing road bridge restoration planning based on the evaluation of resilience

Abstract

This study examined the applicability of a quantum-inspired black-box optimization for determining post-disaster bridge restoration planning in road-bridge networks. The total travel time, obtained using the user equilibrium assignment method, was defined as the function of the network. The objective of this optimization problem is to minimize the area of the resilience triangle. A black-box optimization combining Fujitsu’s digital annealer, genetic algorithms, and factorization machines based on machine learning was employed for the optimization method. The optimal restoration plan was explored using this combined method and a standalone genetic algorithm, commonly applied in past research, for a small-scale virtual road-bridge network including 14 bridges. As a result, the combined method demonstrated an advantage in terms of search efficiency and analysis time, with faster convergence in the early stages of the search compared to the standalone genetic algorithm.